DOP-C02 Exam Dumps - AWS Certified DevOps Engineer - Professional

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.

Amazon Redshift supports audit logging which can be configured to log user activity, including queries and user changes. The logs can be delivered to an S3 bucket (Option B), which is the standard location for Redshift audit logs.

While Redshift can send some logs to CloudWatch, the native audit logging is typically routed to S3. Creating a CloudWatch dashboard with a log widget (Option D) allows visualization of user activities from logs ingested into CloudWatch Logs or through custom processing.

Option A is invalid because CloudTrail does not capture Redshift query logs directly. Option C incorrectly assumes Redshift audit logs can be delivered directly to CloudWatch. Option E adds complexity by requiring Lambda and Athena unnecessarily when direct visualization in CloudWatch is simpler.

Comprehensive and Detailed Explanation From Exact Extract of DevOps Engineer documents only:

The requirement is to shift traffic automatically in equal increments over a defined total deployment time with no manual intervention. In AWS CodeDeploy blue/green deployments for Amazon ECS, the TimeBasedLinear traffic routing option is specifically designed for this purpose.

Option A is correct because:

TimeBasedLinear shifts traffic in equal percentages.

The deployment proceeds automatically at each interval.

linearPercentage defines how much traffic is shifted each time.

linearInterval defines how often the traffic shift happens.

This supports gradual traffic shifting and zero-downtime blue/green deployment behavior.

Why the other options are incorrect:

B. AllAtOnce shifts all traffic immediately, not in equal increments over time.

C. TimeBasedCanary shifts traffic in an initial portion and then the remainder later. That is not equal incremental shifting across the full deployment period.

D. This option is too generic and does not specifically identify the CodeDeploy configuration that provides equal incremental automatic traffic shifting. The exact feature required is TimeBasedLinear.

ï‚· Step 1: Automate Docker Image Deployment using AWS CodePipeline

The first challenge is the manual process of building and deploying Docker images. To address this, you can use AWS CodePipeline to automate the process. AWS CodePipeline integrates with CodeCommit (for source code and Dockerfile storage) and CodeBuild (to build Docker images and store them in Amazon Elastic Container Registry (ECR)).

Action: Create an AWS CodeCommit repository to store the Dockerfile and Kubernetes deployment files. Then, create a pipeline in AWS CodePipeline that triggers on new commits via an Amazon EventBridge event.

Why: This automation significantly reduces the manual effort of building and deploying Docker images when updates are made to the codebase.

https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html

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)

AWS Health provides real-time events and information related to your AWS infrastructure. It can be integrated with Amazon EventBridge to act upon the health events automatically. If the maintenance notification from AWS Health indicates that an EC2 instance requires a restart, you can set up an EventBridge rule to respond to such events. In this case, the target of this rule would be a Lambda function that would trigger a Systems Manager automation to restart the EC2 instance during a maintenance window. Remember, AWS Health is the source of the events (not EC2 or Systems Manager), and AWS Lambda can be used to execute complex remediation tasks, such as scheduling maintenance tasks via Systems Manager.

The following are the steps involved in configuring the EventBridge rule to meet these requirements:

Configure an event source of AWS Health, a service of EC2, and an event type that indicates instance maintenance.

Target a newly created AWS Lambda function that registers an automation task to restart the EC2 instance during a maintenance window.

The AWS Lambda function will be triggered by the event from AWS Health. The function will then register an automation task to restart the EC2 instance during the next maintenance window.

The best solution is to use AWS Config and AWS Security Hub to identify and remediate noncompliant resources across multiple AWS accounts. AWS Config enables continuous monitoring of the configuration of AWS resources and evaluates them against desired configurations. AWS Config can also automatically remediate noncompliant resources by using conformance packs, which are a collection of AWS Config rules and remediation actions that can be deployed as a single entity. AWS Security Hub provides a comprehensive view of the security posture of AWS accounts and resources. AWS Security Hub can aggregate and normalize the findings from AWS Config and other AWS services, as well as from partner solutions. AWS Security Hub can also be used to create a dashboard for tracking noncompliant resources and events in a centralized location.

The other options are not optimal because they either require more development overhead, do not provide near real time detection and remediation, or do not provide a centralized dashboard for tracking.

Option A is not optimal because CloudFormation drift detection is not a near real time solution. Drift detection has to be manually initiated on each stack or resource, or scheduled using a cron expression. Drift detection also does not provide remediation actions, so a custom Lambda function has to be developed and invoked. CloudWatch Logs and dashboard can be used for tracking, but they do not provide a comprehensive view of the security posture of the AWS accounts and resources.

Option B is not optimal because CloudTrail logs analysis using Athena is not a near real time solution. Athena queries have to be manually run or scheduled using a cron expression. Athena also does not provide remediation actions, so a custom Lambda function has to be developed and invoked. Step Functions can be used to orchestrate the query and remediation workflow, but it adds more complexity and cost. QuickSight dashboard can be used for tracking, but it does not provide a comprehensive view of the security posture of the AWS accounts and resources.

Option D is not optimal because CloudTrail logs analysis using CloudWatch Logs is not a near real time solution. CloudWatch Logs filters have to be manually created or updated for each resource type and configuration change. CloudWatch Logs also does not provide remediation actions, so a custom Lambda function has to be developed and invoked. EventBridge can be used to trigger the Lambda function, but it adds more complexity and cost. OpenSearch Service dashboard can be used for tracking, but it does not provide a comprehensive view of the security posture of the AWS accounts and resources.

AWS Config conformance packs

Introducing AWS Config conformance packs

Managing conformance packs across all accounts in your organization