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Amazon DOP-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: SDLC Automation	22%	- CI/CD pipeline design and implementation 1. Build and deployment automation 2. Pipeline optimization and scaling
Topic 2: Monitoring and Logging	15%	- Observability and metrics 1. CloudWatch monitoring and alarms 2. Log aggregation and analysis
Topic 3: Incident and Event Management	18%	- Operational response and recovery 1. Incident detection and remediation 2. Automated event-driven responses
Topic 4: Resilient Cloud Solutions	15%	- High availability and fault tolerance design 1. Disaster recovery strategies 2. Multi-AZ and multi-region architectures
Topic 5: Security and Compliance Automation	13%	- Security automation in CI/CD and infrastructure 1. Compliance monitoring and auditing 2. IAM policy automation and governance
Topic 6: Configuration Management and Infrastructure as Code	17%	- Infrastructure provisioning and automation 1. Configuration tools and automation strategies 2. AWS CloudFormation and CDK usage

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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q169-Q174):

NEW QUESTION # 169
A company uses Amazon API Gateway and AWS Lambda functions to implement an API. The company uses a pipeline in AWS CodePipeline to build and deploy the API. The pipeline contains a source stage, build stage, and deployment stage.
The company deploys the API without performing smoke tests. Soon after the deployment, the company observes multiple issues with the API. A security audit finds security vulnerabilities in the production code.
The company wants to prevent these issues from happening in the future.
Which combination of steps will meet this requirement? (Select TWO.)

A. Add an action in the deployment stage to run an Amazon CodeGuru code scan after deployment.Configure the deployment stage to fail if the scan returns any security findings.
B. Create a smoke test script that returns an error code if the API code fails the test. Add an action in the deployment stage to run the smoke test script after deployment. Configure the deployment stage for automatic rollback.
C. Add an action in the build stage that uses Amazon Inspector to scan the Lambda function code after the code is built. Configure the build stage to fail if the scan returns any security findings. D. Add an action in the build stage to run an Amazon CodeGuru code scan after the code is built. Configure the build stage to fail if the scan returns any security findings.
D. Create a smoke test script that returns an error code if the API code fails the test. Add an action in the deployment stage to run the smoke test script after deployment. Configure the deployment stage to fail if the smoke test script returns an error code.

Answer: A,D

NEW QUESTION # 170
A company has an application that runs on Amazon EC2 instances in an Auto Scaling group. The application processes a high volume of messages from an Amazon Simple Queue Service (Amazon SQS) queue.
A DevOps engineer noticed that the application took several hours to process a group of messages from the SQS queue. The average CPU utilization of the Auto Scaling group did not cross the threshold of a target tracking scaling policy when processing the messages. The application that processes the SQS queue publishes logs to Amazon CloudWatch Logs.
The DevOps engineer needs to ensure that the queue is processed quickly.
Which solution meets these requirements with the LEAST operational overhead?

A. Create a target tracking scaling policy for the Auto Scaling group. In the target tracking policy, use the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupIn-ServiceInstances Auto Scaling group attribute to calculate how many messages are in the queue for each number of instances by using metric math. Use the calculated attribute to scale in and out.
B. Create an AWS Lambda function. Configure the Lambda function to publish a custom metric by using the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupIn-ServiceInstances Auto Scaling group attribute to publish the queue messages for each instance. Schedule an Amazon EventBridge rule to run the Lambda function every hour. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.
C. Create an AWS Lambda function. Configure the Lambda function to publish a custom metric by using the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupIn-ServiceInstances Auto Scaling group attribute to publish the queue messages for each instance. Create a CloudWatch subscription filter for the application logs with the Lambda function as the target. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.
D. Create an AWS Lambda function that logs the ApproximateNumberOfMessagesVisible attribute of the SQS queue to a CloudWatch Logs log group. Schedule an Amazon EventBridge rule to run the Lambda function every 5 minutes. Create a metric filter to count the number of log events from a CloudWatch logs group. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The default CPU utilization metric does not reflect the processing backlog in the SQS queue, so the Auto Scaling group is not scaling properly to handle the workload.
To scale the Auto Scaling group based on queue length, you can create a target tracking scaling policy that uses a custom metric that combines the SQS queue's ApproximateNumberOfMessagesVisible and the number of instances (GroupIn-ServiceInstances) metric using CloudWatch metric math. This allows the scaling policy to calculate the average number of messages per instance and scale accordingly.
This approach requires no additional Lambda functions or log processing, thus minimizing operational overhead.
Option A and B require Lambda functions to publish custom metrics, which increases operational complexity. Option D also adds complexity with logging and metric filters.
Reference:
Scaling based on SQS queue length using metric math:
"You can create CloudWatch metric math expressions combining SQS and Auto Scaling group metrics to enable target tracking scaling policies that respond to queue backlog." (AWS Auto Scaling with SQS) Target Tracking Scaling Policies:
"Target tracking policies can use metric math expressions as a source to make scaling decisions." (AWS Auto Scaling Target Tracking)

NEW QUESTION # 171
A company deploys a web application on Amazon EC2 instances that are behind an Application Load Balancer (ALB). The company stores the application code in an AWS CodeCommit repository. When code is merged to the main branch, an AWS Lambda function invokes an AWS CodeBuild project. The CodeBuild project packages the code, stores the packaged code in AWS CodeArtifact, and invokes AWS Systems Manager Run Command to deploy the packaged code to the EC2 instances.
Previous deployments have resulted in defects, EC2 instances that are not running the latest version of the packaged code, and inconsistencies between instances.
Which combination of actions should a DevOps engineer take to implement a more reliable deployment solution? (Select TWO.)

A. Create a pipeline in AWS CodePipeline that uses the CodeCommit repository as a source provider.
Configure pipeline stages that run the CodeBuild project in parallel to build and test the application. In the pipeline, pass the CodeBuild project output artifact to an AWS CodeDeploy action.
B. Create individual Lambda functions that use AWS CodeDeploy instead of Systems Manager to run build, test, and deploy actions.
C. Create a pipeline in AWS CodePipeline that uses the CodeCommit repository as a source provider.
Create separate pipeline stages that run a CodeBuild project to build and then test the application. In the pipeline, pass the CodeBuild project output artifact to an AWS CodeDeploy action.
D. Create an Amazon S3 bucket. Modify the CodeBuild project to store the packages in the S3 bucket instead of in CodeArtifact. Use deploy actions in CodeDeploy to deploy the artifact to the EC2 instances.
E. Create an AWS CodeDeploy application and a deployment group to deploy the packaged code to the EC2 instances. Configure the ALB for the deployment group.

Answer: A,E

Explanation:
To implement a more reliable deployment solution, a DevOps engineer should take the following actions:
Create a pipeline in AWS CodePipeline that uses the CodeCommit repository as a source provider. Configure pipeline stages that run the CodeBuild project in parallel to build and test the application. In the pipeline, pass the CodeBuild project output artifact to an AWS CodeDeploy action. This action will improve the deployment reliability by automating the entire process from code commit to deployment, reducing human errors and inconsistencies. By running the build and test stages in parallel, the pipeline can also speed up the delivery time and provide faster feedback. By using CodeDeploy as the deployment action, the pipeline can leverage the features of CodeDeploy, such as traffic shifting, health checks, rollback, and deployment configuration123 Create an AWS CodeDeploy application and a deployment group to deploy the packaged code to the EC2 instances. Configure the ALB for the deployment group. This action will improve the deployment reliability by using CodeDeploy to orchestrate the deployment across multiple EC2 instances behind an ALB.
CodeDeploy can perform blue/green deployments or in-place deployments with traffic shifting, which can minimize downtime and reduce risks. CodeDeploy can also monitor the health of the instances during and after the deployment, and automatically roll back if any issues are detected. By configuring the ALB for the deployment group, CodeDeploy can register and deregister instances from the load balancer as needed, ensuring that only healthy instances receive traffic45 The other options are not correct because they do not improve the deployment reliability or follow best practices. Creating separate pipeline stages that run a CodeBuild project to build and then test the application is not a good option because it will increase the pipeline execution time and delay the feedback loop. Creating individual Lambda functions that use CodeDeploy instead of Systems Manager to run build, test, and deploy actions is not a valid option because it will add unnecessary complexity and cost to the solution. Lambda functions are not designed for long-running tasks such as building or deploying applications. Creating an Amazon S3 bucket and modifying the CodeBuild project to store the packages in the S3 bucket instead of in CodeArtifact is not a necessary option because it will not affect the deployment reliability. CodeArtifact is a secure, scalable, and cost-effective package management service that can store and share software packages for application development67
1: What is AWS CodePipeline? - AWS CodePipeline
2: Create a pipeline in AWS CodePipeline - AWS CodePipeline
3: Deploy an application with AWS CodeDeploy - AWS CodePipeline
4: What is AWS CodeDeploy? - AWS CodeDeploy
5: Configure an Application Load Balancer for your blue/green deployments - AWS CodeDeploy
6: What is AWS Lambda? - AWS Lambda
7: What is AWS CodeArtifact? - AWS CodeArtifact

NEW QUESTION # 172
A company recently migrated its legacy application from on-premises to AWS. The application is hosted on Amazon EC2 instances behind an Application Load Balancer which is behind Amazon API Gateway. The company wants to ensure users experience minimal disruptions during any deployment of a new version of the application. The company also wants to ensure it can quickly roll back updates if there is an issue.
Which solution will meet these requirements with MINIMAL changes to the application?

A. Introduce changes as a separate environment parallel to the existing one Configure API Gateway to use a canary release deployment to send a small subset of user traffic to the new environment.
B. Introduce changes as a separate target group behind the existing Application Load Balancer Configure API Gateway to route user traffic to the new target group in steps.
C. Introduce changes as a separate target group behind the existing Application Load Balancer Configure API Gateway to route all traffic to the Application Load Balancer which then sends the traffic to the new target group.
D. Introduce changes as a separate environment parallel to the existing one Update the application's DNS alias records to point to the new environment.

Answer: A

Explanation:
Explanation
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.

NEW QUESTION # 173
A company hosts applications in its AWS account Each application logs to an individual Amazon CloudWatch log group. The company's CloudWatch costs for ingestion are increasing A DevOps engineer needs to Identify which applications are the source of the increased logging costs.
Which solution Will meet these requirements?

A. Use CloudWatch metrics to create a custom expression that Identifies the CloudWatch log groups that have the most data being written to them.
B. Use AWS CloudTrail to filter for CreateLogStream events for each application
C. Use 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
D. Use AWS Cost Explorer to generate a cost report that details the cost for CloudWatch usage

Answer: D

Explanation:
The correct answer is C.
A comprehensive and detailed explanation is:
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.
References:
CloudWatch Metrics
CloudWatch Logs Insights
AWS Cost Explorer
AWS CloudTrail

NEW QUESTION # 174
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