Amazon Web Services DOP-C02 Based on Real Exam Environment

Activate Amazon Macie on the Production S3 Bucket:

Macie can identify and protect sensitive data such as PII.

Create a Step Functions state machine to automate data discovery and redaction before copying it to the development environment.

Example Step Functions state machine:

{

"Comment": "Anonymize PII and copy data",

"StartAt": "MacieDiscoveryJob",

"States": {

"MacieDiscoveryJob": {

"Type": "Task",

"Resource": "arn:aws:states:::macie:startClassificationJob",

"End": true

}

}

}

Create a Development Environment from CloudFormation Template:

Deploy the development environment in a new account using the existing CloudFormation template.

Schedule an EventBridge rule to start the Step Functions state machine on a weekly basis.

EventBridge rule example:

{

"ScheduleExpression": "rate(7 days)",

"StateMachineArn": "arn:aws:states::<account-id>:stateMachine:AnonymizeAndCopyData"

}

By using Macie for data anonymization and Step Functions for automation, you ensure PII is properly handled before data transfer between environments.

Comprehensive and Detailed Explanation From Exact Extract:

AWS CodeDeploy supports automatic rollback capabilities, which allow the deployment to automatically revert to the last known good revision if a failure occurs during the deployment or if health checks fail.

You can enable automatic rollback on CodeDeploy deployments by configuring rollback settings in the deployment group.

Automatic rollback triggers can include failed deployments and alarms failing during the deployment lifecycle, which means if the health check fails, CodeDeploy will automatically roll back without manual intervention.

Lifecycle event hooks (Option A) provide custom scripting capabilities during deployment but do not provide built-in rollback automation.

CloudFormation (Option B) can deploy and manage infrastructure but does not control CodeDeploy rollback.

CloudWatch alarms (Option C) can trigger notifications but require manual or additional automation to trigger rollback.Hence, enabling CodeDeploy’s automatic rollback settings is the simplest and most reliable method for automatic rollback upon health check failure.

Reference from AWS Official Documentation and Study Guide:

AWS CodeDeploy Automatic Rollback:"You can configure automatic rollback in AWS CodeDeploy to roll back a deployment if it fails or if specified CloudWatch alarms are triggered."(AWS CodeDeploy User Guide - Automatic Rollbacks)

AWS Certified DevOps Engineer Professional Study Guide:"Leverage CodeDeploy automatic rollback features to reduce downtime and ensure application stability during deployments."

Verified Answer: A, C, and E.

Short Explanation: To improve the performance of the application, the DevOps engineer should use Amazon RDS Proxy, implement the database connection opening outside the Lambda event handler code, and connect to the proxy endpoint from the Lambda function.

Amazon RDS Proxy is a fully managed, highly available database proxy for Amazon Relational Database Service (RDS) that makes applications more scalable, more resilient to database failures, and more secure1. By using Amazon RDS Proxy, the DevOps engineer can reduce the overhead of opening and closing connections to the database, which can improve latency and throughput2.

The DevOps engineer should connect the proxy to the Aurora cluster reader endpoint, which allows read-only connections to one of the Aurora Replicas in the DB cluster3. This can help balance the load across multiple read replicas and improve performance for read-intensive workloads4.

The DevOps engineer should implement the database connection opening outside the Lambda event handler code, which means using a global variable to store the database connection object5. This can enable connection reuse across multiple invocations of the Lambda function, which can reduce latency and improve performance.

The DevOps engineer should connect to the proxy endpoint from the Lambda function, which is a unique URL that represents the proxy. This can allow the Lambda function to access the database through the proxy, which can provide benefits such as connection pooling, load balancing, failover handling, and enhanced security.

The other options are incorrect because:

Implementing database connection pooling inside the Lambda code is unnecessary and redundant when using Amazon RDS Proxy, which already provides connection pooling as a service.

Implementing the database connection opening and closing inside the Lambda event handler code is inefficient and costly, as it can increase latency and consume more resources for each invocation of the Lambda function.

Connecting to the Aurora cluster endpoint from the Lambda function is not optimal for read-only queries, as it can direct traffic to either the primary instance or one of the Aurora Replicas in the DB cluster. This can result in inconsistent performance and potential conflicts with write operations on the primary instance.