Free and Premium Amazon Web Services DOP-C02 Dumps Questions Answers

To invoke many Lambda functions in parallel and allow each function to have independent retry logic and concurrency management, using SQS queues for each Lambda function is recommended.

The BeginResponse Lambda publishes a message to an SNS topic, which fans out to multiple SQS queues (one per Lambda).

Each Lambda function polls its own SQS queue, allowing fine-grained control of concurrency and retry behavior.

SNS alone (Option A) invokes Lambda functions but lacks the queue’s buffering and retry durability.

Step Functions (Option D) would invoke Lambdas sequentially, not in parallel, and add complexity.

Option C reverses SNS and SQS in an inefficient manner.

EC2 Image Builder is the recommended AWS-native service for automating the creation, validation, and distribution of AMIs. It integrates directly with AWS Organizations, allowing AMIs to be automatically shared with all accounts. The service also supports scheduled builds (e.g., monthly), significantly reducing operational overhead. This design is referenced in AWS documentation: “Automate OS image creation and distribution securely across multiple accounts using EC2 Image Builder.”

 For deploying a Ruby-based application with requirements for interaction with an Amazon RDS for MySQL database, automatic scaling, high availability, and data persistence, the following steps will meet the requirements:

B. Deploy the application on AWS Elastic Beanstalk. Deploy a separate Amazon RDS for MySQL DB instance outside of Elastic Beanstalk. This approach ensures that the database persists independently of the Elastic Beanstalk environment, which can be torn down and recreated without affecting the database123.

E. Use the immutable deployment method to deploy new application versions. Immutable deployments provide a zero-downtime deployment method that ensures that if any part of the deployment process fails, the environment is rolled back to the original state automatically4.

D. Configure an Amazon EventBridge rule to monitor AWS Health events. Use an Amazon Simple Notification Service (Amazon SNS) topic as a target to alert the application team. This setup allows for automated monitoring and alerting of the application team in case of deployment failures or other health events56.

AWS Elastic Beanstalk documentation on deploying Ruby applications1.

AWS documentation on application auto-scaling7.

AWS documentation on automated deployment strategies with automatic rollbacks and alerts456.

Option A is incorrect because EventBridge rules cannot filter events based on the message body or attributes of the target service. Therefore, configuring an SNS subscription filter policy to match the CloudFormation stack will not work. The SNS topic will receive all events from the EventBridge rule, regardless of the stack name or drift status.

Option B is incorrect because it introduces unnecessary complexity and cost. Creating a second Lambda function to query the CloudFormation API for the drift detection results is redundant, since CloudFormation already publishes drift detection events to EventBridge. Moreover, invoking two Lambda functions every hour will incur more charges than invoking one.

Option C is incorrect because GuardDuty does not provide drift detection for CloudFormation stacks. GuardDuty is a threat detection service that monitors for malicious activity and unauthorized behavior in AWS accounts and workloads. It does not monitor or report on configuration changes or drifts in CloudFormation stacks.

Option D is correct because it leverages AWS Config and its managed rule for drift detection. AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources. It can detect configuration changes and drifts in CloudFormation stacks using the cloudformation-stack-drift-detection-check managed rule. This rule triggers an AWS Config event when a stack drifts from its expected template configuration. By creating a second EventBridge rule that reacts to this event for the specific stack, the DevOps engineer can configure the SNS topic as a target and receive a notification as soon as possible when drift is detected.

To meet the requirements of failover and disaster recovery, the company should use the following deployment strategies:

Create an Amazon Aurora global database in two AWS Regions as the data store. In the event of a failure, promote the secondary Region to the primary for the application. Update the application to use the Aurora cluster endpoint in the secondary Region. This strategy can provide a low RPO and RTO for the data, as Aurora global database replicates data with minimal latency across Regions and allows fast and easy failover12. The company can use the Amazon Aurora cluster endpoint to connect to the current primary DB cluster without needing to change any application code1.

Set up the application in two AWS Regions. Configure AWS Global Accelerator to point to Application Load Balancers (ALBs) in both Regions. Add both ALBs to a single endpoint group. Use health checks and Auto Scaling groups in each Region. This strategy can provide high availability and performance for the application, as AWS Global Accelerator uses the AWS global network to route traffic to the closest healthy endpoint3. The company can also use static IP addresses that are assigned by Global Accelerator as a fixed entry point for their application1. By using health checks and Auto Scaling groups, the company can ensure that their application can scale up or down based on demand and handle any instance failures4.

The other options are incorrect because:

Creating an Amazon Aurora Single-AZ cluster in multiple AWS Regions as the data store would not provide a fast failover or disaster recovery solution, as the company would need to manually restore data from backups or snapshots in another Region in case of a failure.

Creating an Amazon Aurora cluster in multiple AWS Regions as the data store and using a Network Load Balancer to balance the database traffic in different Regions would not work, as Network Load Balancers do not support cross-Region routing. Moreover, this strategy would not provide a consistent view of the data across Regions, as Aurora clusters do not replicate data automatically between Regions unless they are part of a global database.

Setting up the application in two AWS Regions and using Amazon Route 53 failover routing that points to Application Load Balancers in both Regions would not provide a low RTO, as Route 53 failover routing relies on DNS resolution, which can take time to propagate changes across different DNS servers and clients. Moreover, this strategy would not provide deterministic routing, as Route 53 failover routing depends on DNS caching behavior, which can vary depending on different factors.

 Step 1: Using AWS IAM Identity Center for SAML-based Identity Federation

To ensure that all users accessing the AWS Management Console are authenticated via the corporate identity provider (IdP), the best approach is to set up identity federation with AWS IAM Identity Center (formerly AWS SSO) using SAML 2.0.

Action: Use AWS IAM Identity Center to configure identity federation with the corporate IdP that supports SAML 2.0.

Why: SAML 2.0 integration enables single sign-on (SSO) for users, allowing them to authenticate through the corporate IdP and gain access to AWS resources.

Option A is the most cost-effective solution. By configuring a warm pool of EC2 instances in the Stopped state, the company can reduce the time it takes for new instances to be ready to serve requests. When the Auto Scaling group launches a new instance, it can attach the stopped EC2 instance from the warm pool. The instance can then be started up immediately, rather than having to wait for the data to be downloaded and processed. This reduces the overall startup time for the application.

AWS Config offers a managed rule called CLOUDFORMATION_STACK_DRIFT_DETECTION_CHECK to detect stack drift automatically.

Creating an SNS topic and subscribing the DevOps lead enables simple notification without custom Lambda functions.

An EventBridge rule that triggers on NON_COMPLIANT status (when drift is detected) can send alerts directly to SNS.Option B adds complexity by requiring custom rules and Lambda. Option C incorrectly triggers on COMPLIANT, not NON_COMPLIANT. Option D needs a Lambda for email, while SNS can send email directly.Hence, Option A offers the least operational effort.

To meet the requirements, the DevOps engineer needs to configure the CloudWatch alarm to stop the EC2 instances when the average of the NetworkPacketsIn metric is less than 5 for at least 3 hours in a 12-hour time window. This means that the alarm should trigger when 3 out of 12 datapoints are below the threshold of 5. The alarm should also treat missing data as not breaching the threshold, so that the EC2 instances continue to run if there is no data for the metric during the evaluation period. The DevOps engineer can add an EC2 action to stop the instance when the alarm enters the ALARM state, which is a built-in action type for CloudWatch alarms.

 Create an Amazon Elastic File System (Amazon EFS) File System with Targets in Multiple Availability Zones:

Amazon EFS provides a scalable and highly available network file system that supports the NFS protocol. EFS is ideal for Linux instances as it allows multiple instances to read and write data concurrently.

Setting up EFS with targets in multiple Availability Zones ensures high availability and durability.

The question focuses on detecting supply chain attacks that bypass CI/CD controls and result in malicious runtime behavior in the deployed serverless application. This requires runtime threat detection, not just static scanning or infrastructure validation.

Amazon GuardDuty is AWS’s managed threat detection service. GuardDuty now includes Lambda Protection, which analyzes Lambda function behavior, including network calls, IAM API usage, and other signals to detect compromised functions, crypto-mining, data exfiltration, and other malicious activity. GuardDuty automatically ingests CloudTrail, VPC Flow Logs (if enabled), and other telemetry without requiring code changes or agents. Findings are published and can be routed to Amazon EventBridge for near real-time notifications or automated remediation.

Option A (AWS WAF) only inspects HTTP requests to API Gateway and is focused on known bad patterns, not deeper behavioral anomalies. Option C (CloudFormation Guard) is focused on infrastructure-as-code validation in the pipeline, which explicitly does not help once malicious code is deployed. Option D (Network Firewall with Emerging Threats rules) protects VPC traffic, but serverless components like Lambda and API Gateway may not all traverse that firewall, and it is not targeted at function-level behavior.

Hence, enabling GuardDuty with Lambda Protection and using EventBridge notifications is the correct solution.

For cross-account deployments, CodePipeline must assume a role in the target account. Configure S3 bucket policy to allow the target account (CodeDeployAccount) access, trust relationship in DevOps_Role to allow assumption by the pipeline’s account (PipelineAccount), and update CodePipeline_Service_Role with sts:AssumeRole permissions. This cross-account pipeline setup is documented in “Cross-Account Access for AWS CodePipeline.”

To implement governance controls that restrict AWS service usage to within the United States and ensure alerts for any activity outside the governance policy, the following actions will meet the requirements:

A. Create an AWS Organizations SCP that denies access to all non-global services in non-US Regions. Attach the policy to the root of the organization. This action will effectively prevent users and roles in all accounts within the organization from accessing services in non-US Regions12.

B. Configure AWS CloudTrail to send logs to Amazon CloudWatch Logs and enable it for all Regions. Use a CloudWatch Logs metric filter to send an alert on any service activity in non-US Regions. This action will allow monitoring of all AWS Regions and will trigger alerts if any activity is detected in non-US Regions, ensuring that the governance team is notified as soon as possible3.

AWS Documentation on Service Control Policies (SCPs) and how they can be used to manage permissions and restrict access based on Regions12.

AWS Documentation on monitoring CloudTrail log files with Amazon CloudWatch Logs to set up alerts for specific activities3.

ALBs with Auto Scaling across AZs ensure high availability in each Region.

Aurora global database supports cross-Region read replicas with low latency for reads and is recommended for multi-Region high availability.

Route 53 latency-based routing sends users to the closest Region.

RDS without global database (Options B, D) has higher replication lag and less resilience.

Using CloudFront in front of ALBs (Options C and D) adds caching but is not required for latency-based routing and increases complexity.

Verify No SCP Blocking Access:

Ensure that no Service Control Policy (SCP) is blocking access for developers to the S3 bucket. SCPs are applied at the organization or organizational unit (OU) level in AWS Organizations and can restrict what actions users and roles in the affected accounts can perform.

Verify No IAM Policy Permissions Boundaries Blocking Access:

IAM permissions boundaries can limit the maximum permissions that a user or role can have. Verify that these boundaries are not restricting access to the S3 bucket.

Make Necessary Changes to SCP and IAM Policy Permissions Boundaries:

Adjust the SCPs and IAM permissions boundaries if they are found to be the cause of the access issue. Make sure these changes are reflected in the code maintained in the AWS CodeCommit repository.

Invoke Deployment Through CloudFormation:

Commit the updated policies to the CodeCommit repository.

Use AWS CloudFormation to deploy the changes across the relevant accounts and resources to ensure that the updated permissions are applied consistently.

By ensuring no SCPs or IAM policy permissions boundaries are blocking access and making necessary changes if they are, the DevOps engineer can resolve the access issue for developers trying to access the S3 bucket.

Option A is correct because using batch writes to write multiple log events in a single write operation is a recommended practice for optimizing the performance and cost of data ingestion in Timestream. Batch writes can reduce the number of network round trips and API calls, and can also take advantage of parallel processing by Timestream. Batch writes can also improve the compression ratio of data in the memory store and the magnetic store, which can reduce the storage costs and improve the query performance1.

Option B is incorrect because writing each log event as a single write operation is not a recommended practice for optimizing the performance and cost of data ingestion in Timestream. Writing each log event as a single write operation would increase the number of network round trips and API calls, and would also reduce the compression ratio of data in the memory store and the magnetic store. This would increase the storage costs and degrade the query performance1.

Option C is incorrect because treating each log as a single-measure record is not a recommended practice for optimizing the query performance in Timestream. Treating each log as a single-measure record would result in creating multiple records for each timestamp, which would increase the storage size and the query latency. Moreover, treating each log as a single-measure record would require using joins to query multiple measures for the same timestamp, which would add complexity and overhead to the query processing2.

Option D is correct because treating each log as a multi-measure record is a recommended practice for optimizing the query performance in Timestream. Treating each log as a multi-measure record would result in creating a single record for each timestamp, which would reduce the storage size and the query latency. Moreover, treating each log as a multi-measure record would allow querying multiple measures for the same timestamp without using joins, which would simplify and speed up the query processing2.

Option E is incorrect because configuring the memory store retention period to be longer than the magnetic store retention period is not a valid option in Timestream. The memory store retention period must always be shorter than or equal to the magnetic store retention period. This ensures that data is moved from the memory store to the magnetic store before it expires out of the memory store3.

Option F is correct because configuring the memory store retention period to be shorter than the magnetic store retention period is a valid option in Timestream. The memory store retention period determines how long data is kept in the memory store, which is optimized for fast point-in-time queries. The magnetic store retention period determines how long data is kept in the magnetic store, which is optimized for fast analytical queries. By configuring these retention periods appropriately, you can balance your storage costs and query performance according to your application needs3.

Mutual TLS (mTLS) authentication requires two-way authentication between the client and the server. For Amazon API Gateway, you can enable mTLS for a custom domain name, which requires clients to present X.509 certificates to verify their identity to access your API. To set up mTLS, you would typically use AWS Certificate Manager (ACM) to create a private certificate authority (CA) and provision a client certificate signed by this private CA. The root CA certificate is then uploaded to an Amazon S3 bucket and configured in API Gateway as the trust store12.

Introducing mutual TLS authentication for Amazon API Gateway1.

Configuring mutual TLS authentication for a REST API2.

AWS Private Certificate Authority details3.

AWS Certificate Manager Private Certificate Authority updates4.

CodeCommit generates events in CloudWatch, CloudWatch triggers the CodeBuild

This solution will meet the requirements in the most operationally efficient manner because it will use CloudWatch Logs destination to aggregate the log groups from all the accounts to a single S3 bucket in the logging account. However, unlike option A, this solution will create a CloudWatch Logs destination for each region, instead of a single destination for all regions. This will improve the performance and reliability of the log delivery, as it will avoid cross-region data transfer and latency issues. Moreover, this solution will use an Amazon Kinesis data stream and an Amazon Kinesis Data Firehose delivery stream for each region, instead of a single stream for all regions. This will also improve the scalability and throughput of the log delivery, as it will avoid bottlenecks and throttling issues that may occur with a single stream.

The company needs fast, proactive alerts for future performance issues, beyond basic EC2 metrics. Option A provides a complete, AWS-native monitoring pattern aligned with best practices:

Enable detailed monitoring on EC2 instances to increase metric resolution (from 5-minute to 1-minute intervals), improving the responsiveness of alarms.

Define custom CloudWatch metrics for application-level indicators such as request latency, error rate, queue depth, or throughput. These metrics can be published from the application or sidecar agents.

Create CloudWatch alarms on both infrastructure (CPU, network, disk) and custom application metrics with thresholds that reflect performance SLOs. Alarms can notify teams via SNS or incident management tools.

Use CloudWatch Logs Insights to analyze logs for recurring error patterns, slow requests, or exceptions when alarms fire.

Option B focuses on tracing and frontend RUM; while useful, it is more complex and not necessary just to get quick alerts. Option C uses services (CloudTrail, GuardDuty, Trusted Advisor) that are not focused on real-time performance detection. Option D with VPC Flow Logs is network-level and would not detect general application performance issues.

Thus, Option A offers a direct, efficient way to detect and alert on performance degradations quickly.

S3 RTC ensures ≤15-min replication SLA.

Multi-Region Access Point (MRAP) simplifies active-passive replication access.

SubmitMultiRegionAccessPointRoutes API manages routing changes during failover.This configuration follows AWS DR best practices for S3 active/passive architectures.

Create an Amazon EventBridge (Amazon CloudWatch Events) rule that matches an AWS Config evaluation result of NON_COMPLIANT for the restricted-ssh rule. Configure an input transformer for the EventBridge (CloudWatch Events) rule. Configure the EventBridge (CloudWatch Events) rule to publish a notification to the SNS topic. This approach uses Amazon EventBridge (previously known as Amazon CloudWatch Events) to filter AWS Config evaluation results based on the restricted-ssh rule and its compliance status (NON_COMPLIANT). An input transformer can be used to customize the information contained in the notification, such as the name and ID of the noncompliant security group. The EventBridge (CloudWatch Events) rule can then be configured to publish a notification to the SNS topic, which will notify the appropriate personnel in real-time.

To allow developers to create IAM users without granting excessive permissions, the correct solution is to use permissions boundaries, which AWS specifically recommends for restricting delegated administrators such as developers. A permissions boundary defines the maximum permissions that an IAM user or role can delegate.

Step C ensures that each AWS account contains a PermissionBoundaries policy defining the maximum allowed permissions that any developer-created user may receive. This prevents privilege escalation, even if the developer attaches a more powerful policy. This aligns with AWS guidance for restricting privilege escalation within multi-account environments.

Step E ensures that developers can create IAM users but only if they attach the PermissionBoundaries policy as the permissions boundary. By attaching the DeveloperBoundary policy to the CreateAndManageUsers role, developers gain the ability to create users, but they are cryptographically prevented from assigning permissions outside the boundary policy.

Meanwhile, the DevOps team (who are not restricted by the boundary) can still create IAM users with full permissions.

This combination satisfies all constraints:

DevOps team: unrestricted IAM creation

Developers: restricted IAM creation enforced by boundaries

Other users: still blocked from IAM creation by existing SCP

Option A is the most correct because it provides both: (1) automated patching and (2) compliance monitoring/enforcement across a fleet, using AWS-native services built for exactly this purpose.

AWS Systems Manager Patch Manager is designed to automate patching of managed instances using patch baselines, maintenance windows (or on-demand), and it produces compliance status for patching. It’s the standard AWS service to apply OS/security patches at scale without SSH’ing into instances.

AWS Config can be used to evaluate and track compliance over time against defined rules, giving centralized visibility and continuous compliance assessment. With remediation, Config can invoke Systems Manager Automation documents to correct non-compliant resources or trigger patch actions (depending on the rule/remediation design). This meets the “monitor and enforce” requirement.

Why the other options don’t meet requirements as well:

B is manual, doesn’t scale well, and increases operational risk (key management, human error). It’s not “automated monitoring and enforcement.”

C (replacing instances with new AMIs) can be part of an immutable infrastructure strategy, but by itself it does not provide compliance monitoring across the current fleet, and scaling policies based on CloudWatch metrics are unrelated to patch compliance. Also, patch cadence would depend on AMI pipelines and instance rotation rather than direct compliance enforcement.

D is operationally heavy and mismatched: CloudTrail records API activity; it does not natively provide “patch compliance” status for instance OS packages. Recreating instances via CloudFormation for every patch is not an efficient or standard enforcement mechanism for patch compliance.

The following are the steps that the company can take to change the log level dynamically when the deployment occurs:

Create a script that uses the CodeDeploy environment variable DEPLOYMENT_GROUP_NAME to identify which deployment group the instance is part of.

Use this information to configure the log level settings.

Reference this script as part of the BeforeInstall lifecycle hook in the appspec.yml file.

The DEPLOYMENT_GROUP_NAME environment variable is automatically set by CodeDeploy when the deployment is triggered. This means that the script does not need to call the metadata service or the EC2 API to identify the deployment group.

This solution is the least complex and requires the least management overhead. It also does not require different script versions for each deployment group.

The following are the reasons why the other options are not correct:

Option A is incorrect because it would require tagging the Amazon EC2 instances, which would be a manual and time-consuming process.

Option C is incorrect because it would require creating a custom environment variable for each environment. This would be a complex and error-prone process.

Option D is incorrect because it would use the DEPLOYMENT_GROUP_ID environment variable. However, this variable is not automatically set by CodeDeploy, so the script would need to call the metadata service or the EC2 API to get the deployment group ID. This would add complexity and overhead to the solution.

The requirement is to deploy a stateless application with multi-Region fault tolerance, ensuring high availability even if an entire AWS Region becomes unavailable. For this design, traffic must be actively served from both Regions, not only during a failure event.

Option C correctly implements an active-active, multi-Region architecture. By deploying the application to both EKS clusters, each Region is capable of serving traffic independently. Using Amazon Route 53 weighted routing, traffic is distributed across both Application Load Balancers, allowing both Regions to handle requests simultaneously. If one Region becomes unhealthy, Route 53 health checks can stop routing traffic to that Region, maintaining availability.

Implementing Kubernetes readiness and liveness probes ensures that traffic is only sent to healthy pods within each cluster. This provides fault tolerance at both the container level (pod health) and the Regional level (Route 53 routing).

Option A uses a failover routing policy, which results in an active-passive design. While fault tolerant, it does not utilize both Regions simultaneously and provides slower recovery during Region failure. Options B and D deploy the application only in the primary Region, which does not meet multi-Region fault tolerance requirements.

Therefore, Option C delivers the most resilient, highly available, and AWS-recommended architecture for a stateless, multi-Region EKS application.

Use routing configuration on an alias to send a portion of traffic to a second function version. For example, you can reduce the risk of deploying a new version by configuring the alias to send most of the traffic to the existing version, and only a small percentage of traffic to the new version.

The following are the steps involved in the deploy stage configuration that will meet the requirements:

Use AWS CodeBuild to add sample event payloads for testing to the Lambda functions.

Publish a new version of the functions, and include Amazon CloudWatch alarms.

Update the production alias to point to the new version.

Configure rollbacks to occur when an alarm is in the ALARM state.

This configuration will help to reduce the customer impact of an unsuccessful deployment by deploying the new version of the functions to a staging environment first. This will allow the DevOps engineer to test the new version of the functions before deploying it to production.

The configuration will also help to monitor for issues by including Amazon CloudWatch alarms. These alarms will alert the DevOps engineer if there are any problems with the new version of the functions.

AWS Config’s ec2-instance-profile-attached managed rule checks for attached instance profiles.

Config supports automatic remediation via Systems Manager Automation runbooks.

This provides continuous compliance with minimal operational overhead.

EventBridge and Lambda (A) require custom coding and risk missing existing instances.

StartInstances (C) does not cover RunInstances and new instances.

IAM-role managed policy check (D) does not check instance profile attachments.

AWS Organizations is a service that helps to manage multiple AWS accounts. AWS Control Tower is a service that makes it easy to set up and govern secure, compliant multi-account AWS environments. Account Factory for Terraform (AFT) is an AWS Control Tower feature that provisions new accounts using Terraform templates. To provision new accounts with the Enterprise Support plan, the DevOps engineer can set the aft_feature_enterprise_support feature flag to True in the AFT deployment input configuration. This flag enables the Enterprise Support plan for newly provisioned accounts.

Account Factory Customization (AFC) automates Control Tower account provisioning with baseline blueprints for networking, logging, and guardrails. It integrates natively and minimizes overhead, per AWS Control Tower documentation.

The company’s requirements span preventive governance, account baseline configuration, and continuous compliance monitoring, all with minimal operational overhead. AWS-native, organization-level services are the most efficient way to meet these goals.

To ensure that new accounts do not retain default VPCs and that development is restricted to specific Regions, AWS Control Tower is the correct foundation. However, Control Tower alone does not remove default VPCs. By installing Customizations for AWS Control Tower (CfCT), the company can automatically deploy OU-scoped CloudFormation templates during account provisioning. A simple CloudFormation template can delete default VPCs in all Regions, while Control Tower-managed Region deny guardrails (SCPs) restrict access to only approved Regions. This approach is declarative, repeatable, and tightly integrated with account creation workflows, resulting in low operational overhead.

For CIS AWS Foundations Benchmark compliance monitoring, AWS Security Hub is the purpose-built service. Enabling Security Hub at the organization level and selecting the CIS benchmark automatically evaluates all member accounts against CIS controls and continuously reports findings. This provides centralized visibility and compliance reporting without custom rule development.

Option C introduces custom Lambda automation and EventBridge logic, increasing maintenance burden. Option E is incorrect because Control Tower does not provide full CIS benchmark monitoring; it only offers related detective guardrails.

Therefore, Option B (Control Tower + CfCT) and Option D (Security Hub with CIS benchmark) together provide the most efficient, scalable, and AWS-recommended solution.

CloudWatch metric filters can parse logs directly to create metrics without additional infrastructure.

Metric filters combined with CloudWatch dashboards provide the simplest and most operationally efficient solution.

Options A, B, and D add complexity with additional services (OpenSearch, Lambda, Kinesis).

To archive CloudWatch Logs to S3 with long-term retention, you need:

a streaming mechanism to move data from CloudWatch Logs to S3, and

an S3 Lifecycle policy to handle tiering and expiration.

Option B uses a CloudWatch Logs subscription filter that targets Amazon Data Firehose. Firehose provides managed, scalable streaming from CloudWatch Logs to S3 with optional buffering and transformation. This is the AWS-recommended pattern for exporting continuous log streams with minimal operational overhead. Option C is not valid because CloudWatch Logs subscription filters cannot directly target S3; they must target Kinesis, Firehose, or Lambda.

Once logs are in S3, the company wants to keep them rarely accessed after 90 days and retained for 10 years. Option D configures an S3 lifecycle policy to transition logs to S3 Glacier Instant Retrieval after 90 days, which is a low-cost archival tier with relatively fast access, and to expire (delete) logs after 3,650 days (10 years). This precisely matches the retention requirement.

Option E uses Reduced Redundancy, which is a legacy storage class and not optimized for long-term archival. Therefore, the correct combination is B and D.

A. The CloudFormation template should be modified to include a parameter that indicates the location of the .zip file containing the Lambda function's code. This allows the CloudFormation deploy action to use the correct artifact depending on the region. This is critical because Lambda functions need to reference their code artifacts from the same region they are being deployed in. B. You would also need to create a new CloudFormation deploy action for the us-east-1 Region within the pipeline. This action should be configured to use the CloudFormation template from the artifact that was specifically created for us-east-1.

The original revision termination settings are configured to wait 1 hour after traffic has been rerouted before terminating the blue task set.

The requirement is to update the infrastructure to ensure that only the Lambda function’s execution role can access the values in Secrets Manager. The solution must apply the principle of least privilege, which means granting the minimum permissions necessary to perform a task.

To do this, the DevOps engineer needs to use the following steps:

Create a KMS customer managed key that trusts Secrets Manager and allows the Lambda function’s execution role to decrypt. A customer managed key is a symmetric encryption key that is fully managed by the customer. The customer can define the key policy, which specifies who can use and manage the key. By creating a customer managed key, the DevOps engineer can restrict the decryption permission to only the Lambda function’s execution role, and prevent other principals from accessing the secret values. The customer managed key also needs to trust Secrets Manager, which means allowing Secrets Manager to use the key to encrypt and decrypt secrets on behalf of the customer.

Update Secrets Manager to use the new customer managed key. Secrets Manager allows customers to choose which KMS key to use for encrypting each secret. By default, Secrets Manager uses the default KMS key for Secrets Manager, which is a service-managed key that is shared by all customers in the same AWS Region. By updating Secrets Manager to use the new customer managed key, the DevOps engineer can ensure that only the Lambda function’s execution role can decrypt the secret values using that key.

Ensure that the Lambda function’s execution role has the KMS permissions scoped on the resource level. The Lambda function’s execution role is an IAM role that grants permissions to the Lambda function to access AWS services and resources. The role needs to have KMS permissions to use the customer managed key for decryption. However, to apply the principle of least privilege, the role should have the permissions scoped on the resource level, which means specifying the ARN of the customer managed key as a condition in the IAM policy statement. This way, the role can only use that specific key and not any other KMS keys in the account.

The requirement is to prevent any product files containing unannounced product IDs (prefixed with a specific UUID) from being stored in the production S3 bucket that users can access.

To achieve this, a best practice is to use a staging bucket as a control point before files go to production, combined with Amazon Macie’s data classification capabilities.

Creating a custom data identifier in Amazon Macie allows precise detection of product IDs starting with the specific UUID, which default managed identifiers will not detect.

By running a Macie sensitive data discovery job on the staging bucket, you can identify files containing these sensitive product IDs.

Only files without findings (i.e., files that do not contain unannounced product IDs) are copied to the production bucket, ensuring no sensitive information is exposed.This approach aligns with AWS best practices for data classification and staged deployment workflows, maximizing control and reducing risk.Using Macie on the production bucket directly (options B and D) risks exposing sensitive data before detection and deletion. Option C uses managed data identifiers, which will likely not detect the custom UUID prefix pattern.

Reference from AWS Official Documentation and Study Guide:

Amazon Macie Custom Data Identifiers:"You can create custom data identifiers in Amazon Macie to find sensitive data that is unique to your organization."(Amazon Macie User Guide)

Data Security Best Practices:"Use staging environments to inspect and sanitize data before moving it to production to reduce exposure risks."(AWS Security Best Practices)

Amazon ECR supports enhanced scanning powered by Amazon Inspector, which provides deeper security scanning for container images including OS and programming language package vulnerabilities.

Enabling enhanced scanning (Option B) allows detection of CRITICAL and HIGH vulnerabilities.

Amazon ECR emits scan completion events via EventBridge, which can trigger Lambda functions. The Lambda function can process the scan results from Amazon Inspector and programmatically approve or reject the image in the CI/CD pipeline (Option D).

Basic scanning (Option A) is limited and does not integrate with Inspector.

Options C and E describe functionalities not natively supported (ECR does not automatically submit Rejected status; Clair is not used in AWS ECR scanning).

To meet the requirements of creating a new Organizations account structure with an appropriate SCP that supports the use of only services that are currently active in the AWS account, the company should use the following solution:

Create an SCP that allows the services that IAM Access Analyzer identifies. IAM Access Analyzer is a service that helps identify potential resource-access risks by analyzing resource-based policies in the AWS environment. IAM Access Analyzer can also generate IAM policies based on access activity in the AWS CloudTrail logs. By using IAM Access Analyzer, the company can create an SCP that grants only the permissions that are required for the application to run, and denies all other services. This way, the company can enforce the use of only approved AWS services and reduce the risk of unauthorized access12

Create an OU for the account. Move the account into the new OU. An OU is a container for accounts within an organization that enables you to group accounts that have similar business or security requirements. By creating an OU for the account, the company can apply policies and manage settings for the account as a group. The company should move the account into the new OU to make it subject to the policies attached to the OU3

Attach the new SCP to the new OU. Detach the default FullAWSAccess SCP from the new OU. An SCP is a type of policy that specifies the maximum permissions for an organization or organizational unit (OU). By attaching the new SCP to the new OU, the company can restrict the services that are available to all accounts in that OU, including the account that runs the application. The company should also detach the default FullAWSAccess SCP from the new OU, because this policy allows all actions on all AWS services and might override or conflict with the new SCP45

The other options are not correct because they do not meet the requirements or follow best practices. Creating an SCP that denies the services that IAM Access Analyzer identifies is not a good option because it might not cover all possible services that are not approved or required for the application. A deny policy is also more difficult to maintain and update than an allow policy. Creating an SCP that allows the services that IAM Access Analyzer identifies and attaching it to the organization’s root is not a good option because it might affect other accounts and OUs in the organization that have different service requirements or approvals. Creating an SCP that allows the services that IAM Access Analyzer identifies and attaching it to the management account is not a valid option because SCPs cannot be attached directly to accounts, only to OUs or roots.

1: Using AWS Identity and Access Management Access Analyzer - AWS Identity and Access Management

2: Generate a policy based on access activity - AWS Identity and Access Management

3: Organizing your accounts into OUs - AWS Organizations

4: Service control policies - AWS Organizations

5: How SCPs work - AWS Organizations

These solutions will meet the requirement because they will prevent the loss of notification messages in the future. An Amazon SQS queue is a service that provides a reliable, scalable, and secure message queue for asynchronous communication between distributed components. You can use an SQS queue to buffer messages from an SNS topic and ensure that they are delivered and processed by a Lambda function, even if the function or the database is temporarily unavailable.

Option C will configure an SQS dead-letter queue for the SNS topic. A dead-letter queue is a queue that receives messages that could not be delivered to any subscriber after a specified number of retries. You can use a dead-letter queue to store and analyze failed messages, or to reprocess them later. This way, you can avoid losing messages that could not be delivered to the Lambda function due to network errors, throttling, or other issues.

Option D will subscribe an SQS queue to the SNS topic and configure the Lambda function to process messages from the SQS queue. This will decouple the SNS topic from the Lambda function and provide more flexibility and control over the message delivery and processing. You can use an SQS queue to store messages from the SNS topic until they are ready to be processed by the Lambda function, and also to retry processing in case of failures. This way, you can avoid losing messages that could not be processed by the Lambda function due to database errors, timeouts, or other issues.

The most operationally efficient solution is to use AWS Systems Manager Parameter Store1 to store the AMI ID and reference it in the launch template2. This way, the launch template does not need to be updated every time a new AMI is created by Image Builder. Instead, the Image Builder pipeline can update the Parameter Store value with the newest AMI ID3, and the Auto Scaling group can launch instances using the latest value from Parameter Store.

The other solutions require updating the launch template or creating a new version of it every time a new AMI is created, which adds complexity and overhead. Additionally, using EventBridge rules and Lambda functions or Run Command documents introduces additional dependencies and potential points of failure.

 1: AWS Systems Manager Parameter Store 2: Using AWS Systems Manager parameters instead of AMI IDs in launch templates 3: Update an SSM parameter with Image Builder

 Use AWS Step Functions to Orchestrate Processing:

AWS Step Functions allow you to build distributed applications by combining AWS Lambda functions or other AWS services into workflows.

Decoupling the processing into Step Functions tasks enables you to retry individual steps without reprocessing the entire record.

 Architectural Steps:

Create a web application to pass records to AWS Step Functions:

The web application can be a simple frontend that receives input and triggers the Step Functions workflow.

Define a Step Functions state machine:

Each step in the state machine represents a processing stage. If a step fails, Step Functions can retry the step based on defined conditions.

Use AWS Lambda functions:

Lambda functions can be used to handle each processing step. These functions can be stateless and handle specific tasks, reducing the complexity of error handling and reprocessing logic.

 Operational Efficiency:

Using Step Functions and Lambda improves operational efficiency by providing built-in error handling, retries, and state management.

This architecture scales automatically and isolates failures to individual steps, ensuring only failed steps are retried.

The correct answer is C. Allowing users to deploy CloudFormation stacks using AWS Service Catalog only and enforcing the use of a launch constraint is the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only. AWS Service Catalog is a service that enables organizations to create and manage catalogs of IT services that are approved for use on AWS. A launch constraint is a rule that specifies the role that AWS Service Catalog assumes when launching a product. By using a launch constraint, the DevOps engineer can control the permissions that the users have when launching a product. Using AWS Config rules to detect when resources have drifted from their expected state is the best way to automate the monitoring of the resources. AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources. AWS Config rules are custom or managed rules that AWS Config uses to evaluate whether your AWS resources comply with your desired configurations. By using AWS Config rules, the DevOps engineer can track the changes in the resources and identify any non-compliant resources.

Option A is incorrect because allowing users to deploy CloudFormation stacks using a CloudFormation service role only is not the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only. A CloudFormation service role is an IAM role that CloudFormation assumes to create, update, or delete the stack resources. By using a CloudFormation service role, the DevOps engineer can control the permissions that CloudFormation has when acting on the resources, but not the permissions that the users have when launching a stack. Therefore, option A does not prevent the users from launching resources that are not approved by the company. Using CloudFormation drift detection to detect when resources have drifted from their expected state is a valid way to monitor the resources, but it is not as automated and scalable as using AWS Config rules. CloudFormation drift detection is a feature that enables you to detect whether a stack’s actual configuration differs, or has drifted, from its expected configuration. To use this feature, the DevOps engineer would need to manually initiate a drift detection operation on the stack or the stack resources, and then view the drift status and details in the CloudFormation console or API.

Option B is incorrect because allowing users to deploy CloudFormation stacks using a CloudFormation service role only is not the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only, as explained in option A. Using AWS Config rules to detect when resources have drifted from their expected state is a valid way to monitor the resources, as explained in option C.

Option D is incorrect because enforcing the use of a template constraint is not the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only. A template constraint is a rule that defines the values or properties that users can specify when launching a product. By using a template constraint, the DevOps engineer can control the parameters that the users can provide when launching a product, but not the permissions that the users have when launching a product. Therefore, option D does not prevent the users from launching resources that are not approved by the company. Using Amazon EventBridge notifications to detect when resources have drifted from their expected state is a less reliable and consistent solution than using AWS Config rules. Amazon EventBridge is a service that enables you to connect your applications with data from a variety of sources. Amazon EventBridge can deliver a stream of real-time data from event sources, such as AWS services, and route that data to targets, such as AWS Lambda functions. However, to use this solution, the DevOps engineer would need to configure the event source, the event bus, the event rule, and the event target for each resource type that needs to be monitored, which is more complex and error-prone than using AWS Config rules.

To meet the requirements, the DevOps team needs to configure a monitoring solution for the VPC flow logs that can detect anomalies in network traffic over time and initiate a response to the anomaly. The DevOps team can use Amazon Kinesis Data Streams to ingest and process streaming data from CloudWatch Logs. The DevOps team can subscribe the log group to a Kinesis data stream, which will deliver log events from CloudWatch Logs to Kinesis Data Streams in near real-time. The DevOps team can then create an AWS Lambda function to detect log anomalies using machine learning or statistical methods. The Lambda function can be set as a processor for the data stream, which means that it will process each record from the stream before sending it to downstream applications or destinations. The Lambda function can also write to the default Amazon EventBridge event bus if it detects an anomaly, which will allow other AWS services or custom applications to respond to the anomaly event.

Step 1: Understanding the Deletion FailureThe most likely reason why the CloudFormation stack failed to delete is that the S3 bucket was not empty. AWS CloudFormation cannot delete an S3 bucket that contains objects, so if the website files are still in the bucket, the deletion will fail.

Issue: The S3 bucket is not empty during deletion, preventing the stack from being deleted.

Step 2: Modifying the Custom Resource to Handle DeletionTo mitigate this issue, you can modify the Lambda function associated with the custom resource to automatically empty the S3 bucket when the stack is being deleted. By adding logic to handle the RequestType: Delete event, the function can recursively delete all objects in the bucket before allowing the stack to be deleted.

Action: Modify the Lambda function to recursively delete the objects in the S3 bucket when RequestType is set to Delete.

Why: This ensures that the S3 bucket is empty before CloudFormation tries to delete it, preventing the stack deletion failure.

The correct answer is C.

Option A is incorrect because creating a new SCP that has two statements, one that allows access to the new range of IP addresses for all the S3 buckets and one that denies access to the old range of IP addresses for all the S3 buckets, is not a valid solution. SCPs are not resource-based policies, and they cannot specify the S3 buckets or the IP addresses as resources or conditions. SCPs can only control the actions that can be performed by the principals in the organization, not the access to specific resources. Moreover, setting a permissions boundary for the OrganizationAccountAccessRole role in the two OUs to deny access to the S3 buckets is not sufficient to revoke the permissions of the two OUs, as there might be other roles or users in those OUs that can still access the S3 buckets.

Option B is incorrect because creating a new SCP that has a statement that allows only the new range of IP addresses to access the S3 buckets is not a valid solution, for the same reason as option A. SCPs are not resource-based policies, and they cannot specify the S3 buckets or the IP addresses as resources or conditions. Creating another SCP that denies access to the S3 buckets and attaching it to the two OUs is also not a valid solution, as SCPs cannot specify the S3 buckets as resources either.

Option C is correct because it meets both requirements of changing the range of IP addresses that have permission to access the contents of the S3 buckets and revoking the permissions of two OUs in the company. On all the S3 buckets, configuring resource-based policies that allow only the new range of IP addresses to access the S3 buckets is a valid way to update the IP address ranges, as resource-based policies can specify both resources and conditions. Creating a new SCP that denies access to the S3 buckets and attaching it to the two OUs is also a valid way to revoke the permissions of those OUs, as SCPs can deny actions such as s3:PutObject or s3:GetObject on any resource.

Option D is incorrect because setting a permissions boundary for the OrganizationAccountAccessRole role in the two OUs to deny access to the S3 buckets is not sufficient to revoke the permissions of the two OUs, as there might be other roles or users in those OUs that can still access the S3 buckets. A permissions boundary is a policy that defines the maximum permissions that an IAM entity can have. However, it does not revoke any existing permissions that are granted by other policies.

B. Update the CodeBuild project role with the necessary permissions and then remove the AWS credentials from the environment variable. C. Store the DB_PASSWORD as a SecureString value in AWS Systems Manager Parameter Store and then remove the DB_PASSWORD from the environment variables. E. Use AWS Systems Manager run command versus scp and ssh commands directly to the instance.

AWS CodeDeploy supports deployment of both Lambda functions and ECS services with native integration.

It provides deployment strategies such as canary deployments for Lambda and blue/green deployments for ECS, which minimize downtime and risk.

CodeDeploy automates the deployment process, which reduces manual effort and improves consistency.

AWS Systems Manager Parameter Store is a simple, managed solution for storing configuration data securely and at scale.

Option A uses CloudFormation stack updates with resource replacement, which can cause downtime and is less flexible for minimizing downtime compared to CodeDeploy’s deployment strategies.

Option C adds unnecessary orchestration complexity with Step Functions and cross-region deployments, increasing deployment effort.

Option D’s all-at-once deployments for Lambda and rolling updates for ECS do not minimize downtime as effectively as canary and blue/green strategies.Hence, option B meets the requirement with the least deployment effort and maximizes automation and availability.

Reference from AWS Official Documentation:

Deploying Lambda Functions with CodeDeploy:"AWS CodeDeploy supports canary and linear deployment strategies for AWS Lambda functions to reduce deployment risk."(CodeDeploy Lambda Deployment)

Deploying ECS Services with CodeDeploy Blue/Green:"CodeDeploy supports blue/green deployments for ECS to minimize downtime during service updates."(CodeDeploy ECS Blue/Green)

AWS Systems Manager Parameter Store:"Parameter Store provides secure, hierarchical storage for configuration data management."(Systems Manager Parameter Store)

Mounting EFS to EC2-backed EKS nodes requires:

NFS (port 2049) open from nodes to EFS (Security Group rule).

Mount targets in each subnet/AZ where nodes reside.

IAM role for the EFS CSI driver with elasticfilesystem:ClientMount and ClientRootAccess permissions.These are the standard setup requirements in “Using the Amazon EFS CSI Driver with Amazon EKS.”

Use a Service Control Policy (SCP) with a Null condition on lambda:VpcIds to deny Lambda function creation or update when not VPC-attached. This enforces compliance across all accounts automatically without manual remediation, aligning with AWS Control Tower governance recommendations.

When using the awslogs log driver with Amazon ECS on EC2, CloudWatch Logs permissions must be granted to the ECS container instance IAM role, not the task definition or infrastructure role. The ECS agent running on the EC2 instances is responsible for creating log streams and pushing log events to Amazon CloudWatch Logs on behalf of the containers.

In this scenario, the task definition is correctly configured to automatically create the log group (awslogs-create-group: true) and send logs to the specified Region. However, the error occurs because the EC2 container instances do not have sufficient IAM permissions to perform the required CloudWatch Logs API calls. As a result, ECS cannot place the task, and it reports that no container instance meets the requirements.

According to AWS documentation, the container instance IAM role must include the following permissions when using the awslogs driver:

logs:CreateLogStream

logs:PutLogEvents

(and, when creating log groups automatically) logs:CreateLogGroup

Option C correctly addresses the root cause by updating the container instance IAM role. Option A is incorrect because the ECS infrastructure or service role is not used to write logs. Option B is unrelated to permissions and does not resolve the issue. Option D would fail because the log group does not already exist, causing task startup to fail.

Therefore, modifying the container instance IAM role is the correct solution.

The correct answer is C.

Option A is incorrect because using CloudWatch metrics to create a custom expression that identifies the CloudWatch log groups that have the most data being written to them is not a valid solution. CloudWatch metrics do not provide information about the size or volume of data being ingested by CloudWatch logs. CloudWatch metrics only provide information about the number of events, bytes, and errors that occur within a log group or stream. Moreover, creating a custom expression with CloudWatch metrics would require using the search_web tool, which is not necessary for this use case.

Option B is incorrect because using CloudWatch Logs Insights to create a set of queries for the application log groups to identify the number of logs written for a period of time is not a valid solution. CloudWatch Logs Insights can help analyze and filter log events based on patterns and expressions, but it does not provide information about the cost or billing of CloudWatch logs. CloudWatch Logs Insights also charges based on the amount of data scanned by each query, which could increase the logging costs further.

Option C is correct because using AWS Cost Explorer to generate a cost report that details the cost for CloudWatch usage is a valid solution. AWS Cost Explorer is a tool that helps visualize, understand, and manage AWS costs and usage over time. AWS Cost Explorer can generate custom reports that show the breakdown of costs by service, region, account, tag, or any other dimension. AWS Cost Explorer can also filter and group costs by usage type, which can help identify the specific CloudWatch log groups that are the source of the increased logging costs.

Option D is incorrect because using AWS CloudTrail to filter for CreateLogStream events for each application is not a valid solution. AWS CloudTrail is a service that records API calls and account activity for AWS services, including CloudWatch logs. However, AWS CloudTrail does not provide information about the cost or billing of CloudWatch logs. Filtering for CreateLogStream events would only show when a new log stream was created within a log group, but not how much data was ingested or stored by that log stream.

The best solution to log and review all stopped tasks for errors is to use Amazon EventBridge and Amazon CloudWatch Logs. Amazon EventBridge allows the DevOps engineer to create a rule that matches task state change events from Amazon ECS. The rule can then send the event data to Amazon CloudWatch Logs as the target. Amazon CloudWatch Logs can store and monitor the log data, and also provide CloudWatch Logs Insights, a feature that enables the DevOps engineer to interactively search and analyze the log data. Using CloudWatch Logs Insights, the DevOps engineer can filter and aggregate the log data based on various fields, such as cluster, task, container, and reason. This way, the DevOps engineer can easily identify and investigate the stopped tasks and their errors.

The other options are not as effective or efficient as the solution in option A. Option B is not suitable because the embedded metric format is designed for custom metrics, not for logging task state changes. Option C is not feasible because the EC2 instances do not store the task state change events in their logs. Option D is not relevant because the EC2_INSTANCE_TERMINATING lifecycle hook is triggered when an EC2 instance is terminated by the Auto Scaling group, not when a task is stopped by Amazon ECS.

Creating a CloudWatch Events Rule That Triggers on an Event - Amazon Elastic Container Service

Sending and Receiving Events Between AWS Accounts - Amazon EventBridge

Working with Log Data - Amazon CloudWatch Logs

Analyzing Log Data with CloudWatch Logs Insights - Amazon CloudWatch Logs

Embedded Metric Format - Amazon CloudWatch

Amazon EC2 Auto Scaling Lifecycle Hooks - Amazon EC2 Auto Scaling

Amazon CloudWatch metric streams are designed to deliver metrics in near real time to downstream services like Amazon Data Firehose with minimal configuration and management overhead. By configuring a metric stream to include all metrics, you ensure that any newly created performance or custom metrics are automatically included without needing to update filters or code when namespaces or metrics change.

Option B uses a CloudWatch metric stream → Firehose → Lambda → S3 pattern. Firehose natively supports invoking a Lambda function for in-flight transformation of the metric data before final delivery to S3. This satisfies the requirement to transform metrics before storing them and provides a fully managed, serverless pipeline with very little ongoing operational burden.

Option A mentions including metrics from “the application and the CloudWatch namespace,” which implies more manual selection and maintenance, potentially missing new metrics if not explicitly added. Options C and D work at the log level, not at the metric level, and would require building additional logic to derive metrics from logs. This is more complex and not aligned with “least operational overhead.”

Therefore, Option B is the most efficient and scalable solution.

 Step 1: Creating a Centralized Domain in the Shared Services Account

To manage application package dependencies across multiple accounts, the most efficient solution is to create a centralized domain in the shared services account. This allows all application teams to access and manage package repositories within the same domain, ensuring consistency and centralization.

Action: Create a domain in the shared services account.

Why: A single, centralized domain reduces the need for redundant management in each application team's account.

For pull through cache repositories, Amazon ECR now supports repository creation templates that can be applied to a registry prefix, such as /external. These templates define default settings, including encryption configuration with a specific KMS key, tag immutability, scan on push, and more. When new cache repositories are auto-created under that prefix, they inherit the template settings automatically.

In this scenario, existing external cache repositories are noncompliant because they use SSE-S3. The company can delete those repositories (removing the noncompliant caches) and configure an ECR repository creation template for the /external prefix that specifies the required customer managed KMS key. As new images are pulled, ECR recreates the cache repositories under that prefix with KMS encryption using the specified key, guaranteeing compliance going forward.

Option A (AWS Config) would only detect noncompliance after creation and cannot enforce encryption at creation time. Option C (SCP) cannot directly control repository encryption properties. Option D misuses EventBridge; KMS cannot be a “target” that retroactively encrypts repositories.

Therefore, using an ECR repository creation template with the desired KMS key is the correct, automatic, and secure solution.

API Gateway supports canary deployment on a deployment stage before you direct all traffic to that stage. A parallel environment means we will create a new ALB and a target group that will target a new set of EC2 instances on which the newer version of the app will be deployed. So the canary setting associated to the new version of the API will connect with the new ALB instance which in turn will direct the traffic to the new EC2 instances on which the newer version of the application is deployed.

A- D - This option correctly utilizes AWS CodePipeline to invoke the CodeBuild job and create CloudFormation change sets. It adds a manual approval step before executing the change sets and starting the AWS CodeDeploy deployment. This ensures that the deployment process is automated while retaining the final manual approval step.

The correct answer is C. Configuring AWS CloudTrail to send log data events to an Amazon CloudWatch Logs log group and creating a CloudWatch logs metric filter to match failed ConsoleLogin events is the simplest and most efficient way to monitor and alert on failed login attempts. Creating a CloudWatch alarm that is based on the metric filter and configuring an alarm action to send messages to the SNS topic will ensure that the security team is notified when multiple failed login attempts occur. This solution requires the least operational effort compared to the other options.

Option A is incorrect because it involves configuring AWS CloudTrail to send log management events instead of log data events. Log management events are used to track changes to CloudTrail configuration, such as creating, updating, or deleting a trail. Log data events are used to track API activity in AWS accounts, such as login attempts. Therefore, option A will not capture the failed ConsoleLogin events.

Option B is incorrect because it involves creating an Amazon Athena query and two Amazon EventBridge rules to monitor and alert on failed login attempts. This is a more complex and costly solution than using CloudWatch logs and alarms. Moreover, option B relies on the query returning a failure, which may not happen if the query is executed successfully but does not find any failed logins.

Option D is incorrect because it involves configuring AWS CloudTrail to send log data events to an Amazon S3 bucket and configuring an Amazon S3 event notification for the s3:ObjectCreated event type. This solution will not work because the s3:ObjectCreated event type does not allow filtering by ConsoleLogin failed events. The event notification will be triggered for any object created in the S3 bucket, regardless of the event type. Therefore, option D will generate a lot of false positives and unnecessary notifications.

AWS CloudTrail Log File Examples

Creating CloudWatch Alarms for CloudTrail Events: Examples

Monitoring CloudTrail Log Files with Amazon CloudWatch Logs

This solution will meet the requirements because it will use a CloudFormation custom resource to execute custom logic during the stack set operation. A custom resource is a resource that you define in your template and that is associated with an AWS Lambda function. The Lambda function runs whenever the custom resource is created, updated, or deleted, and can perform any actions that are supported by the AWS SDK. In this case, the Lambda function can use the GuardDuty API to check whether GuardDuty is already enabled in each target account, and if not, enable it. This way, the DevOps engineer can avoid deploying the stack set to accounts that already have GuardDuty enabled, and prevent failure during the deployment.

ECR pull-through cache caches images from upstream repositories for resilience.

Private repo with basic scanning ensures vulnerability detection on pushed images.

Enabling pull-through caching on a private repo combines caching and vulnerability scanning seamlessly.

Public repos do not support pull-through caching of upstream images.

Replication rules are for multi-Region replication, not upstream caching.

ECR Lifecycle Policies automatically manage image retention based on tag status, age, or count. They execute natively within the ECR service, requiring no external management or scripts — the least overhead and AWS-recommended cleanup method.

A: If the ApproximateAgeOfOldestMessages indicate that orders are remaining in the SQS queue for longer than expected, the reserved concurrency limit may be set too small to keep up with the number of orders entering the queue and is being throttled. D: The DynamoDB table is using Auto Scaling. With Auto Scaling, you create a scaling policy that specifies whether you want to scale read capacity or write capacity (or both), and the minimum and maximum provisioned capacity unit settings for the table. The ThottledWriteRequests metric will indicate if there is a throttling issue on the DynamoDB table, which can be resolved by increasing the maximum write capacity units for the table's Auto Scaling policy.

AWS CodeArtifact is a fully managed artifact repository service that supports popular package formats and can proxy downstream public repositories securely. It integrates seamlessly with both AWS Cloud and on-premises environments and stores artifacts in highly durable object storage (Amazon S3). CodeArtifact encrypts data at rest and in transit by default.

Because there is currently no routing between the on-premises data center and AWS, establishing a third-party software VPN appliance for connectivity provides secure communication without requiring complex AWS Direct Connect or multiple VPN setups.

This solution can be deployed quickly (within two weeks), is highly available, and meets encryption requirements. Using CodeArtifact eliminates the need to maintain third-party artifact servers, reducing operational overhead and improving reliability.

Options B, C, and D introduce higher complexity with AWS Direct Connect or multiple VPN connections and third-party software that increases deployment time and operational burden, which is against the requirement for rapid deployment and high operational efficiency.

Reference from AWS Official Documentation:

AWS CodeArtifact Overview:"CodeArtifact is a fully managed artifact repository service that makes it easy for organizations to securely store, publish, and share software packages used in their software development process."(AWS CodeArtifact Documentation)

Encryption in AWS CodeArtifact:"CodeArtifact encrypts data at rest and in transit by default."(AWS CodeArtifact Security)

VPN Appliance Deployment:"Software VPN appliances are commonly used for secure, encrypted connectivity between on-premises data centers and AWS."(AWS VPN Options)

This hook runs before the new application version is installed on the replacement instances. This is the best place to run the script because it ensures that the license file is downloaded and installed before the replacement instances start to handle request traffic. If you use any other hook, you may encounter errors or inconsistencies in your application.

because of "as few maintenance tasks as possible on the underlying infrastructure". Fargate does that better than "one centralized application server"

To meet the requirements of deploying a workload on several hundred EC2 instances with least-privilege permissions and temporary security credentials, the company should use an IAM role and an instance profile. An IAM role is a way to grant permissions to an entity that you trust, such as an EC2 instance. An instance profile is a container for an IAM role that you can use to pass role information to an EC2 instance when the instance starts. By using an IAM role and an instance profile, the EC2 instances can automatically receive temporary security credentials from the AWS Security Token Service (STS) and use them to access the S3 buckets. This way, the company does not need to manage or rotate any long-term credentials, such as IAM users or access keys.

To use an IAM role and an instance profile, the company should create an IAM role that has the appropriate permissions for S3 buckets. The permissions should allow the EC2 instances to read from the source S3 bucket and write to the destination S3 bucket. The company should also create a trust policy for the IAM role that specifies that EC2 is allowed to assume the role. Then, the company should add the IAM role to an instance profile. An instance profile can have only one IAM role, so the company does not need to create multiple roles or profiles for this scenario.

Next, the company should update the launch template to include the IAM instance profile. A launch template is a way to save launch parameters for EC2 instances, such as the instance type, security group, user data, and IAM instance profile. By using a launch template, the company can ensure that all EC2 instances in the Auto Scaling group have consistent configuration and permissions. The company should specify the name or ARN of the IAM instance profile in the launch template. This way, when the Auto Scaling group launches new EC2 instances based on the launch template, they will automatically receive the IAM role and its permissions through the instance profile.

The other options are not correct because they do not meet the requirements or follow best practices. Creating an IAM user and generating a secret key and token is not a good option because it involves managing long-term credentials that need to be rotated regularly. Moreover, embedding credentials in user data is not secure because user data is visible to anyone who can describe the EC2 instance. Creating a trust anchor and profile is not a valid option because trust anchors are used for certificate-based authentication, not for IAM roles or instance profiles. Modifying user data to use a new secret key and token is also not a good option because it requires updating user data every time the credentials change, which is not scalable or efficient.

1: AWS Certified DevOps Engineer - Professional Certification | AWS Certification | AWS

2: DevOps Resources - Amazon Web Services (AWS)

3: Exam Readiness: AWS Certified DevOps Engineer - Professional

IAM Roles for Amazon EC2 - AWS Identity and Access Management

Working with Instance Profiles - AWS Identity and Access Management

Launching an Instance Using a Launch Template - Amazon Elastic Compute Cloud

Temporary Security Credentials - AWS Identity and Access Management

The company requires an organization-wide, centralized, and automated solution to detect sensitive data in Amazon S3, aggregate findings in one location, and quarantine affected objects with minimal operational overhead. AWS provides a native service specifically designed for this purpose: Amazon Macie.

Option A is essential because Amazon Macie automatically discovers and classifies sensitive data such as PII in S3 buckets using managed machine learning models. When enabled at the organization level, Macie scans buckets across all accounts and Regions. Integrating Macie with AWS Security Hub centralizes all findings in a single dashboard, allowing the company’s security officer to review and manage sensitive data alerts across the organization without building custom aggregation pipelines.

Detection alone is not sufficient; remediation is also required. Option C completes the solution by using Amazon EventBridge, which natively receives Macie findings in near real time. An EventBridge rule can trigger a Lambda function whenever Macie identifies sensitive data. The Lambda function can then copy the affected object to a quarantine S3 bucket and delete the original object, meeting the remediation requirement automatically and consistently.

Option D requires custom sensitive data detection logic, which is complex, error-prone, and unnecessary given Macie’s capabilities. Option E is invalid because SCPs cannot inspect or move data. Option B does not detect sensitive information.

Therefore, A and C together provide the most efficient, scalable, and AWS-recommended solution.

When you create a pipeline from CodePipeline during the step-by-step it creates a CloudWatch Event rule for a given branch and repo

like this:

{

"source": [

"aws.codecommit"

],

"detail-type": [

"CodeCommit Repository State Change"

],

"resources": [

"arn:aws:codecommit:us-east-1:xxxxx:repo-name"

],

"detail": {

"event": [

"referenceCreated",

"referenceUpdated"

],

"referenceType": [

"branch"

],

"referenceName": [

"master"

]

}

}

service catalog uses stacksets and can enforce tag and restrict resources AWS Customer case with tag enforcement And Youtube video showing how to restrict resources per user with portfolio

The current design colocates the web tier, database, and cache on the same EC2 instances. This causes resource contention and makes it difficult to scale each layer independently. The correct solution is to separate these concerns into independently scalable managed services.

Option D uses an Application Load Balancer (ALB) in front of an Auto Scaling group for the web application. ALB is designed for HTTP/HTTPS traffic, supports path-based and host-based routing, and can spread load evenly across multiple instances and Availability Zones, eliminating the issue where one instance is overloaded.

The database is migrated to Amazon Aurora with Multi-AZ, which provides high availability, automated failover, and managed backups. Aurora offloads database management and ensures consistent performance.

For caching, Option D introduces Amazon ElastiCache for Redis, a managed in-memory cache that is purpose-built for low-latency reads, independent scaling, and high availability through replication and clustering.

Option A and C misuse NLB for HTTP traffic or for Redis exposure and introduce unnecessary complexity. Option B keeps Redis on EC2 (via NLB + ASG in a single AZ), which reduces availability and does not leverage managed caching.

Thus, Option D best separates tiers and improves performance and availability.

The correct answer is B and C. Option B is correct because inviting the acquired company’s AWS accounts to join the organization and creating the OrganizationAccountAccessRole IAM role in the invited accounts allows the management account to assume the role and gain full administrative access to the member accounts. Option C is correct because using AWS Security Hub to collect and group findings across all accounts enables the DevOps team to monitor and improve the security posture of the organization. Security Hub can automatically detect new accounts as the accounts are added to the organization and enable Security Hub for them. Option A is incorrect because creating an SCP that has full administrative privileges and attaching it to the management account does not grant the management account access to the member accounts. SCPs are used to restrict the permissions of the member accounts, not to grant permissions to the management account. Option D is incorrect because using AWS Firewall Manager to collect and group findings across all accounts is not a valid use case for Firewall Manager. Firewall Manager is used to centrally configure and manage firewall rules across the organization, not to collect and group security findings. Option E is incorrect because using Amazon Inspector to collect and group findings across all accounts is not a valid use case for Amazon Inspector. Amazon Inspector is used to assess the security and compliance of applications running on Amazon EC2 instances, not to collect and group security findings across accounts. References:

Inviting an AWS account to join your organization

Enabling and disabling AWS Security Hub

Service control policies

AWS Firewall Manager

Amazon Inspector

The startup delay occurs because large container images must be pulled from Amazon ECR when pods are scheduled, especially on newly added nodes that do not have cached images. To consistently reduce pod startup time to seconds, container images must be prefetched directly onto the worker nodes that run the pods, not the EKS control plane.

Amazon EKS control plane nodes are fully managed by AWS and cannot be accessed or modified using AWS Systems Manager. Therefore, any solution that attempts to run Systems Manager commands on control plane nodes is invalid. Prefetching must target the EKS worker nodes (EC2 instances in managed node groups) where container images are actually stored and used.

Option C correctly creates an IAM role that allows Amazon EventBridge to invoke AWS Systems Manager to run commands on the EKS worker nodes. By using Systems Manager State Manager associations with node tags, the automation dynamically targets both existing nodes and newly added nodes that inherit the same tags. This ensures that container images are pulled immediately when a new image is pushed to ECR or when new nodes join the cluster.

Option B incorrectly targets nodes based on machine size, which is not reliable for lifecycle automation. Options A and D incorrectly reference control plane nodes, which cannot be managed with Systems Manager.

Therefore, Option C is the correct and AWS-aligned solution to ensure fast pod startup times across all nodes.

To aggregate logs from multiple accounts in an organization, the DevOps engineer needs to create a cross-account subscription1 that allows the monitoring account to receive log events from the sharing accounts.

To enable cross-account subscription, the DevOps engineer needs to create an IAM role in each sharing account that grants permission to CloudWatch Logs to link the log groups to the destination in the monitoring account2. This can be done using a CloudFormation template and StackSets3 to deploy the role to all accounts in the organization.

The DevOps engineer also needs to create an IAM role in the monitoring account that allows CloudWatch Logs to create a sink for receiving log events from other accounts4. The role must have a trust policy that specifies the organization ID as a condition.

Finally, the DevOps engineer needs to attach the CloudWatchLogsReadOnlyAccess policy5 to an IAM role in the monitoring account that can be used to search the logs from the cross-account subscription.

 1: Cross-account log data sharing with subscriptions 2: Create an IAM role for CloudWatch Logs in each sharing account 3: AWS CloudFormation StackSets 4: Create an IAM role for CloudWatch Logs in your monitoring account 5: CloudWatchLogsReadOnlyAccess policy

If instead you want to automatically apply the policy to existing in-scope resources, choose Auto remediate any noncompliant resources. This option creates a web ACL in each applicable account within the AWS organization and associates the web ACL with the resources in the accounts. When you choose Auto remediate any noncompliant resources, you can also choose to remove existing web ACL associations from in-scope resources, for the web ACLs that aren't managed by another active Firewall Manager policy. If you choose this option, Firewall Manager first associates the policy's web ACL with the resources, and then removes the prior associations. If a resource has an association with another web ACL that's managed by a different active Firewall Manager policy, this choice doesn't affect that association.

CF of network exports the VPC, subnet or needed information CF of application imports the above information to its stack and UpdateChangeSet/ ExecuteChangeSet

AWS Secrets Manager is a secrets management service that helps you protect access to your applications, services, and IT resources. This service enables you to easily rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle. Using Secrets Manager, you can secure and manage secrets used to access resources in the AWS Cloud, on third-party services, and on-premises. SSM parameter store and AWS Secret manager are both a secure option. However, Secrets manager is more flexible and has more options like password generation. Reference:

To meet the requirements of migrating its on-premises Windows and Linux applications to AWS and creating a pilot light DR environment in another AWS Region, the company should use Amazon FSx for NetApp ONTAP for the application storage. Amazon FSx for NetApp ONTAP is a fully managed service that provides highly reliable, scalable, high-performing, and feature-rich file storage built on NetApp’s popular ONTAP file system. FSx for ONTAP supports multiple protocols, including SMB for Windows and NFS for Linux, so the company can access the shared storage from both types of applications. FSx for ONTAP also supports NetApp SnapMirror replication, which enables the company to replicate the storage to the DR Region. NetApp SnapMirror replication is efficient, secure, and incremental, and it preserves the data deduplication and compression benefits of FSx for ONTAP. The company can use automation to launch and configure the EC2 instances in the DR Region and then use NetApp SnapMirror to restore the data from the primary Region.

The other options are not correct because they do not meet the requirements or follow best practices. Using Amazon S3 for the application storage is not a good option because S3 is an object storage service that does not support SMB or NFS protocols natively. The company would need to use additional services or software to mount S3 buckets as file systems, which would add complexity and cost. Using Amazon EBS for the application storage is also not a good option because EBS is a block storage service that does not support SMB or NFS protocols natively. The company would need to set up and manage file servers on EC2 instances to provide shared access to the EBS volumes, which would add overhead and maintenance. Using a Volume Gateway in AWS Storage Gateway for the application storage is not a valid option because Volume Gateway does not support SMB protocol. Volume Gateway only supports iSCSI protocol, which means that only Linux applications can access the shared storage.

1: What is Amazon FSx for NetApp ONTAP? - FSx for ONTAP

2: Amazon FSx for NetApp ONTAP

3: Amazon FSx for NetApp ONTAP | NetApp

4: AWS Announces General Availability of Amazon FSx for NetApp ONTAP

Replicating Data with NetApp SnapMirror - FSx for ONTAP

What Is Amazon S3? - Amazon Simple Storage Service

What Is Amazon Elastic Block Store (Amazon EBS)? - Amazon Elastic Compute Cloud

What Is AWS Storage Gateway? - AWS Storage Gateway

Step 1: Using Service Control Policies (SCPs) for EC2 SecurityTo limit the use of EC2 instance credentials to the specific EC2 instance they are assigned to, you can create a Service Control Policy (SCP) that verifies specific conditions, such as whether the EC2 instance's source VPC and private IP match expected values.

Action: Create an SCP that checks whether the values of the aws:EC2InstanceSourceVPC and aws:SourceVpc condition keys are the same. Deny access if they are not.

Why: This ensures that credentials cannot be used outside the designated EC2 instance or VPC.

Step 2: Further Validation with Private IPsThe SCP should also verify that the EC2 instance's private IP matches the IP range specified for the VPC. If the instance's private IP does not match, access should be denied.

Action: In the same SCP, check whether the values of the aws:EC2InstanceSourcePrivateIP and aws:VpcSourceIP condition keys are the same. Deny access if they are not.

Why: This ensures that the credentials are only used within the specific EC2 instance and its associated VPC.

To aggregate events from multiple accounts into a single account, the default event bus in the receiving (DevOps) account must have a resource-based policy allowing the source accounts to put events into it.

Then, an EventBridge rule in each source account routes Amazon Connect events to the default event bus in the DevOps account (cross-account event delivery).

Options A and B describe policies or rules incorrectly applying permissions or routing. Option D mentions replay permissions, which are unrelated to event routing.

 Create an Amazon EventBridge Rule Using an AWS CloudTrail Event Pattern:

AWS CloudTrail logs API calls made in your account, including actions performed by roles.

Create an EventBridge rule that matches CloudTrail events where the AssumeRole API call is made to assume the administrator role.

 Invoke an AWS Lambda Function:

Configure the EventBridge rule to trigger a Lambda function whenever the rule's conditions are met.

The Lambda function will handle the logic to send a notification.

 Publish a Message to an Amazon SNS Topic:

The Lambda function will publish a message to an SNS topic to notify the security team.

Subscribe the security team’s email address to this SNS topic to receive real-time notifications.

Example EventBridge rule pattern:

{

"source": ["aws.cloudtrail"],

"detail-type": ["AWS API Call via CloudTrail"],

"detail": {

"eventSource": ["sts.amazonaws.com"],

"eventName": ["AssumeRole"],

"requestParameters": {

"roleArn": ["arn:aws:iam::<account-id>:role/AdministratorRole"]

}

}

}

Example Lambda function (Node.js) to publish to SNS:

const AWS = require('aws-sdk');

const sns = new AWS.SNS();

exports.handler = async (event) => {

const params = {

Message: `Administrator role assumed: ${JSON.stringify(event.detail)}`,

TopicArn: 'arn:aws:sns::<account-id>:'

};

await sns.publish(params).promise();

};

Amazon EventBridge can directly consume AWS Health events as an event source. You can specify event types such as “AWS_EC2_INSTANCE_RETIREMENT_SCHEDULED” or “AWS_EC2_INSTANCE_TERMINATION_SCHEDULED.” The rule’s target should invoke the Systems Manager Automation document (runbook) AWS-RestartEC2Instance to automate the restart within a defined maintenance window. This event-driven automation pattern is described in AWS documentation: “Automating AWS Health event remediation using EventBridge and Systems Manager.”

The recommended architecture to audit, analyze, and visualize application workload metrics at scale involves:

Using Amazon EventBridge to schedule AWS Lambda invocations that call the application API and fetch metrics (Option A). The data is stored in Amazon S3, which is ideal for scalable, cost-effective storage of large datasets.

Cataloging the stored data with AWS Glue crawlers, enabling schema discovery and making data queryable via Amazon Athena (Option C).

Visualizing the data by creating Amazon QuickSight datasets from Athena views and building dashboards for analysis (Option E).Option B and D introduce DynamoDB, which is less suitable for large-scale analytics and Athena querying. Option F suggests querying Athena via Lambda widgets in CloudWatch, which adds complexity without significant benefit over QuickSight.

CodePipeline V2 with QUEUED mode ensures that new executions wait for the previous execution to finish, preventing overlapping runs.

Adding a trigger filter to listen for Git tags triggers the pipeline only on new tag pushes.

An EventBridge rule can detect new image pushes to ECR and start the deployment pipeline, integrating the build and deploy pipelines effectively.

SUPERSEDED mode cancels the previous run when a new one starts, which is not desired here.

Using branches instead of tags would trigger on all commits, not just releases.

To restrict direct access to API Gateway, add a custom header in the CloudFront distribution origin request and use a Lambda authorizer in API Gateway to validate that header. Only requests routed through CloudFront will include this header. This is the AWS-recommended pattern in “Secure your APIs behind CloudFront using custom headers and Lambda authorizers.”

AWS Systems Manager Patch Manager automates patch scanning and installation. Integrating with AWS Config enables compliance reporting and auto-remediation. This provides centralized patch compliance management per AWS Ops best practices.

Amazon S3 can generate server access logs that record detailed information about each request, including requester, bucket, key, operation, time, and status. These logs are written as objects to an S3 bucket. To analyze access patterns, the simplest and most serverless approach is to use Amazon Athena directly on those logs without building ingestion pipelines or databases.

Option B enables S3 server access logging and then creates an Athena external table over the log bucket. AWS provides standard log formats and even example schemas for S3 access logs. The analytics team can run ad hoc SQL queries to count the number of accesses per object per time period, filter by user, and perform aggregations, all without provisioning compute or managing databases.

Option A requires ingesting logs into Aurora, which adds ETL complexity and ongoing database management. Option C requires a Lambda function for every access event plus DB writes, which is more complex and potentially expensive at scale. Option D uses CloudWatch Logs and Managed Flink, which is more suited for streaming analytics and is significantly more complex than necessary for monthly summary reports.

Therefore, Option B provides the required analysis with the least development and operational effort.

When using the awslogs log driver with ECS on EC2, the ECS agent running on the container instance uses the instance’s IAM role (container instance role or task execution role, depending on configuration) to write logs to CloudWatch Logs. The policy already grants logs:CreateLogGroup, logs:CreateLogStream, and logs:PutLogEvents, which are the required CloudWatch Logs actions. However, for the role to be usable by ECS, the role’s trust policy must allow the appropriate service principal to assume it.

In this question, the error message indicates “missing permission” during ECS task deployment. If the IAM role is not trusted by the ECS service (for example, ecs-tasks.amazonaws.com for a task execution role or the proper principal for container instances), ECS cannot assume that role and therefore cannot use the granted CloudWatch permissions, causing deployment failures.

Option A addresses this by adding a trust relationship so that Amazon ECS can assume the IAM role. Options B and C mutate the permissions but do not fix the underlying problem: the missing trust. Option D incorrectly attempts to trust CloudWatch, which does not assume roles in this context.

Thus, adding a trust policy that establishes ECS as a trusted service is the correct fix.

The “fan-out” batch build type in AWS CodeBuild allows running multiple builds in parallel for different subsets of tests, reducing overall test execution time. CodeBuild then aggregates the results into a single consolidated report. This approach is detailed in the AWS documentation under “Batch builds and parallel execution with CodeBuild.”

AWS CloudWatch Events can be used to monitor events across different AWS resources, and a CloudWatch Event Rule can be created to trigger an AWS Lambda function when a deployment issue is detected in the pipeline. The Lambda function can then evaluate the issue and send a notification to the appropriate stakeholders through an Amazon SNS topic. This approach allows for real-time notifications and faster resolution times.

To implement a control that requires the use of IMDSv2 on all EC2 instances in the account, the DevOps engineer can use a permissions boundary. A permissions boundary is a policy that defines the maximum permissions that an IAM entity can have. The DevOps engineer can create a permissions boundary that prevents the ec2:RunInstance action if the ec2:MetadataHttpTokens condition key is not set to a value of required. This condition key enforces the use of IMDSv2 on EC2 instances. The DevOps engineer can attach the permissions boundary to the IAM role that was used to launch the instance. This way, any attempt to launch an EC2 instance without using IMDSv2 will be denied by the permissions boundary.