Amazon Web Services SAP-C02 Exam With Confidence Using Practice Dumps

The correct solution is to use the authenticate-oidc action for the ALB listener rule and configure it with the details of the AWS Directory Service for Microsoft Active Directory directory. This way, the ALB can use OpenID Connect (OIDC) to authenticate users against the directory and grant them access to the intranet web application. The app client in the directory is used to register the ALB as an OIDC client and provide the necessary credentials and endpoints. The callback URL is the URL that the ALB redirects the user to after a successful authentication. This solution does not require any additional services or roles, and it leverages the existing directory accounts for all users.

The other solutions are incorrect because they either use the wrong action for the ALB listener rule, or they involve unnecessary or incompatible services or roles. For example:

Solution B is incorrect because it uses Amazon Cognito user pool, which is a separate user directory service that does not integrate with AWS Directory Service for Microsoft Active Directory. To use this solution, the company would have to migrate or synchronize their users from the directory to the user pool, which is not required by the question. Moreover, the authenticate-cognito action for the ALB listener rule only works with Amazon Cognito user pools, not with federated identity providers (IdPs) that have metadata from the directory.

Solution C is incorrect because it uses IAM as an identity provider (IdP), which is not compatible with AWS Directory Service for Microsoft Active Directory. IAM can only be used as an IdP for web identity federation, which allows users to sign in with social media or other third-party IdPs, not with Active Directory. Moreover, the authenticate-oidc action for the ALB listener rule requires an OIDC IdP, not a SAML 2.0 federation IdP, which is what IAM provides.

Solution D is incorrect because it uses AWS IAM Identity Center (AWS Single Sign-On), which is a service that simplifies the management of SSO access to multiple AWS accounts and business applications. This service is not needed for the scenario in the question, which only involves a single intranet web application. Moreover, the authenticate-cognito action for the ALB listener rule does not work with external IdPs that use SAML, such as AWS IAM Identity Center.

Authenticate users using an Application Load Balancer

What is AWS Directory Service for Microsoft Active Directory?

Using OpenID Connect for user authentication

Comprehensive and Detailed Explanation:

This question is mainly about cost optimization for two different workload patterns:

A constant-rate ingestion workload

A noncritical nightly batch analytics workload that can tolerate interruption or rerun

The most cost-effective architecture is to decouple storage from the ingestion servers, store the incoming data in Amazon S3, and run the nightly calculations by using AWS Batch on Spot Instances.

Why B is correct

Amazon Data Firehose can continuously ingest streaming data and deliver the data to Amazon S3. Because the data rate is constant, the company does not need to keep a fleet of EC2 instances running just to collect and retain data on attached EBS volumes. Using S3 as durable storage is operationally simpler and generally more cost-effective than maintaining long-running EC2 ingestion servers with EBS-based storage for this type of streaming landing zone.

For the nightly processing, the job is:

batch oriented,

takes only 4 hours,

not critical to the business,

acceptable to rerun later if interrupted.

These characteristics make Spot Instances an ideal fit. AWS Batch is specifically designed to run batch computing workloads and can efficiently provision and manage compute resources, including Spot Instances. Since the job can tolerate interruption, Spot capacity provides the lowest-cost compute option.

This option reduces cost in both major areas:

storage and ingestion are moved to managed services and object storage,

nightly compute moves from On-Demand Instances to Spot-based batch processing.

Why A is incorrect

A improves the design by moving ingestion to Data Firehose and storage to S3, which is a good step. However, it still uses EC2 On-Demand Instances for nightly processing. Because the nightly workload is not business-critical and can tolerate failure, Spot Instances are a better fit and more cost-effective than On-Demand Instances. Therefore, A is not the most cost-effective choice.

Why C is incorrect

This option keeps ingestion on a fleet of EC2 Reserved Instances with 3-year reservations behind a Network Load Balancer. That is not the most economical or simplest design for constant ingestion when a managed ingestion service such as Data Firehose can deliver directly to S3.

Also, a Network Load Balancer does not add meaningful value for the ingestion requirement described here. The issue is not distributing interactive network traffic to an application fleet. The better architecture is to use a managed data ingestion service instead of recommitting to long-term EC2 reservations.

While AWS Batch with Spot Instances is good for the nightly analytics portion, the ingestion side of this option is still more expensive and less efficient than B.

Why D is incorrect

Amazon Redshift is a data warehouse and is useful for analytical querying, but this option is not the most cost-effective for the stated use case. The company needs to ingest constant-rate streaming market data and run a nightly aggregate job. Storing all incoming data in Redshift is generally a more specialized and potentially more expensive architecture than landing the raw stream in S3 and running elastic batch processing on demand.

The Lambda part is also problematic. The batch processing currently takes 4 hours. AWS Lambda is not designed for long-running heavy batch jobs of this kind. Even if the aggregate query could be expressed in SQL, the answer is still not the most cost-effective and does not align as well with the workload pattern as S3 plus AWS Batch on Spot Instances.

Key design principles being tested

This question tests whether you can identify:

S3 as low-cost, durable storage for incoming streamed data

Managed ingestion services instead of self-managed EC2 ingestion servers

Spot Instances for interruptible, fault-tolerant batch jobs

AWS Batch as the managed orchestration service for batch workloads

Final justification

The most cost-effective redesign is to:

ingest the streaming data with Amazon Data Firehose,

store the data in Amazon S3,

run nightly analytics through AWS Batch on Spot Instances.

That makes B the best answer.

When an IAM user appears to have the correct permissions but still receives Access Denied errors, especially in an AWS Organizations environment, the effective permissions must be evaluated across all permission layers. In AWS, permissions are the intersection of all applicable controls, and an explicit deny at any layer overrides any allow.

First, because the company uses AWS Organizations, service control policies (SCPs) must be evaluated. SCPs define the maximum permissions that accounts or organizational units can have. Even if an IAM user or IAM policy allows an action, an SCP attached to the account or OU can explicitly or implicitly deny that action. If an SCP does not allow s3:ListAllMyBuckets or related S3 read actions, the IAM user will not be able to list buckets in the S3 console and will see no buckets at all. Therefore, checking the SCPs applied to the OU or account is a required troubleshooting step.

Second, IAM permissions boundaries can further restrict the effective permissions of an IAM user. A permissions boundary defines the maximum permissions that an IAM user can exercise, regardless of what their attached policies allow. If the permissions boundary does not include the required Amazon S3 read-only permissions (such as s3:ListAllMyBuckets or s3:GetBucketLocation), the user will receive access denied errors even though the user’s IAM policy appears to allow read-only access. Reviewing whether a permissions boundary is attached, and whether it allows the necessary S3 actions, is essential.

Option A (checking bucket policies) can be relevant for access to specific buckets or objects, but a user seeing no buckets at all in the S3 console is more commonly caused by missing or denied account-level list permissions rather than individual bucket policies. Bucket policies generally do not control the ability to list all buckets in an account unless they contain explicit denies, which is less common as a first troubleshooting step in an Organizations setup.

Option B (checking ACLs) is less relevant because ACLs primarily control object-level and bucket-level access and are not typically used to manage console-level visibility of all buckets in an account. ACLs also do not commonly block the ListAllMyBuckets action.

Option E is incorrect because IAM users do not require IAM roles to access AWS services. Roles are assumed by users or services, but the presence or absence of a role attached to an IAM user does not affect the user’s direct permissions.

Therefore, the most appropriate troubleshooting steps are to check the SCPs applied through AWS Organizations and to verify whether an IAM permissions boundary is restricting the user’s effective permissions.