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Amazon DOP-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Incident and Event Response	18%	- Design and implement event and incident management 1. Design event aggregation and correlation 2. Implement automated response playbooks 3. Implement automated incident detection - Design and implement chaos engineering practices 1. Implement fault injection experiments (AWS Fault Injection Simulator) 2. Analyze system behavior under failure conditions 3. Design resilience testing strategies
Topic 2: Policies and Standards Automation	10%	- Design and implement governance strategies 1. Implement tagging policies and resource grouping 2. Design cost optimization through policies 3. Implement approval workflows and automation - Design and implement preventive and detective controls 1. Implement drift detection and remediation 2. Implement AWS Organizations and SCPs 3. Design and implement security baselines
Topic 3: Monitoring and Logging	12%	- Design and implement alerting and incident management 1. Create alarm notification strategies 2. Design runbook automation 3. Implement automated incident response - Design and implement monitoring and observability strategies 1. Implement distributed tracing (AWS X-Ray) 2. Implement log aggregation and analysis 3. Design custom metrics and alarms (Amazon CloudWatch)
Topic 4: SDLC Automation	22%	- Design and implement CI/CD pipelines 1. Design failure handling strategies 2. Determine appropriate CI/CD pipeline architecture 3. Implement deployment strategies (blue-green, canary, rolling) 4. Develop CI/CD pipelines considering testing and security requirements - Design and implement source code management strategies 1. Implement repository configurations and hooks 2. Design code review and approval processes 3. Determine branching strategies - Design build and test environments 1. Integrate security scanning and compliance checks 2. Implement build environments (isolated, reproducible) 3. Design test automation frameworks
Topic 5: Configuration Management and Infrastructure as Code	22%	- Design and implement configuration management 1. Implement parameter management (AWS Parameter Store, Secrets Manager) 2. Implement AWS Systems Manager for configuration management 3. Design patch management strategies - Implement compliance and configuration monitoring 1. Use AWS Config for compliance monitoring 2. Design remediation automation 3. Implement AWS CloudTrail for auditing - Design and implement data management strategies 1. Implement data lifecycle management 2. Implement database migration strategies 3. Design backup and recovery solutions - Design and implement infrastructure as code 1. Develop IaC templates (AWS CloudFormation, Terraform) 2. Design for scalability and repeatability 3. Implement modular and reusable infrastructure components
Topic 6: High Availability and Disaster Recovery	16%	- Design and implement disaster recovery strategies 1. Implement multi-region active-active architectures 2. Implement backup and restore mechanisms 3. Implement pilot light and warm standby architectures 4. Design RTO and RPO based DR solutions - Design and implement high availability and scalability 1. Implement load balancing and traffic management 2. Design multi-AZ and multi-region architectures 3. Implement auto scaling strategies - Implement data backup and restore strategies 1. Implement cross-region replication 2. Implement validation testing for backups 3. Design point-in-time recovery solutions

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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q66-Q71):

NEW QUESTION # 66
A development team wants to use AWS CloudFormation stacks to deploy an application. However, the developer IAM role does not have the required permissions to provision the resources that are specified in the AWS CloudFormation template. A DevOps engineer needs to implement a solution that allows the developers to deploy the stacks. The solution must follow the principle of least privilege.
Which solution will meet these requirements?

A. Create an AWS CloudFormation service role that has the required permissions. Grant the developer IAM role the iam:PassRole permission. Use the new service role during stack deployments.
B. Create an IAM policy that allows the developers to provision the required resources. Attach the policy to the developer IAM role.
C. Create an AWS CloudFormation service role that has the required permissions. Grant the developer IAM role a cloudformation:* action. Use the new service role during stack deployments.
D. Create an IAM policy that allows full access to AWS CloudFormation. Attach the policy to the developer IAM role.

Answer: A

NEW QUESTION # 67
A company is developing an application that will generate log events. The log events consist of five distinct metrics every one tenth of a second and produce a large amount of data The company needs to configure the application to write the logs to Amazon Time stream The company will configure a daily query against the Timestream table.
Which combination of steps will meet these requirements with the FASTEST query performance? (Select THREE.)

A. Treat each log as a multi-measure record
B. Use batch writes to write multiple log events in a Single write operation
C. Write each log event as a single write operation
D. Configure the memory store retention period to be longer than the magnetic store retention period
E. Configure the memory store retention period to be shorter than the magnetic store retention period
F. Treat each log as a single-measure record

Answer: A,B,E

Explanation:
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.
References:
1: Batch writes
2: Multi-measure records vs. single-measure records
3: Storage

NEW QUESTION # 68
A company has configured Amazon RDS storage autoscaling for its RDS DB instances. A DevOps team needs to visualize the autoscaling events on an Amazon CloudWatch dashboard. Which solution will meet this requirement?

A. Create an Amazon EventBridge rule that reacts to RDS storage autoscaling events (rom the RDS events. Create a CloudWatch alarm. Configure the EventBridge rule to change the status of the CloudWatch alarm. Visualize the alarm status by using the CloudWatch dashboard.
B. Create a trail by using AWS CloudTrail with data events configured. Configure the trail to send the data events to Amazon CloudWatch Logs. Create a metric filter in CloudWatch Logs to match the RDS storage autoscaling events. Visualize the metric filter by using the CloudWatch dashboard.
C. Create an Amazon EventBridge rule that reacts to RDS storage autoscaling events from RDS events. Create an AWS Lambda function that publishes a CloudWatch custom metric. Configure the EventBridge rule to invoke the Lambda function. Visualize the custom metric by using the CloudWatch dashboard.
D. Create a trail by using AWS CloudTrail with management events configured. Configure the trail to send the management events to Amazon CloudWatch Logs. Create a metric filter in CloudWatch Logs to match the RDS storage autoscaling events. Visualize the metric filter by using the CloudWatch dashboard.

Answer: C

Explanation:
Step 1: Reacting to RDS Storage Autoscaling Events Using Amazon EventBridgeAmazon RDS emits events when storage autoscaling occurs. To visualize these events in a CloudWatch dashboard, you can create an EventBridge rule that listens for these specific autoscaling events.
Action: Create an EventBridge rule that reacts to RDS storage autoscaling events from the RDS event stream.
Why: EventBridge allows you to listen to RDS events and route them to specific AWS services for processing.
Step 2: Creating a Custom CloudWatch Metric via LambdaOnce the EventBridge rule detects a storage autoscaling event, you can use a Lambda function to publish a custom metric to CloudWatch. This metric can then be visualized in a CloudWatch dashboard.
Action: Use a Lambda function to publish custom metrics to CloudWatch based on the RDS storage autoscaling events.
Why: Custom metrics allow you to track specific events like autoscaling and visualize them easily on a CloudWatch dashboard.
Reference:
This corresponds to Option A: Create an Amazon EventBridge rule that reacts to RDS storage autoscaling events from RDS events. Create an AWS Lambda function that publishes a CloudWatch custom metric. Configure the EventBridge rule to invoke the Lambda function. Visualize the custom metric by using the CloudWatch dashboard.

NEW QUESTION # 69
A DevOps engineer is building a multistage pipeline with AWS CodePipeline to build, verify, stage, test, and deploy an application. A manual approval stage is required between the test stage and the deploy stage. The development team uses a custom chat tool with webhook support that requires near-real-time notifications.
How should the DevOps engineer configure status updates for pipeline activity and approval requests to post to the chat tool?

A. Create an Amazon EventBridge rule that filters on CodePipeline Pipeline Execution State Change.
Publish the events to an Amazon Simple Notification Service (Amazon SNS) topic. Create an AWS Lambda function that sends event details to the chat webhook URL. Subscribe the function to the SNS topic.
B. Modify the pipeline code to send the event details to the chat webhook URL at the end of each stage.Parameterize the URL so that each pipeline can send to a different URL based on the pipeline environment.
C. Create an AWS Lambda function that is invoked by AWS CloudTrail events. When a CodePipeline Pipeline Execution State Change event is detected, send the event details to the chat webhook URL.
D. Create an Amazon CloudWatch Logs subscription that filters on CodePipeline Pipeline Execution State Change. Publish subscription events to an Amazon Simple Notification Service (Amazon SNS) topic.
Subscribe the chat webhook URL to the SNS topic, and complete the subscription validation.

Answer: A

Explanation:
https://aws.amazon.com/premiumsupport/knowledge-center/sns-lambda-webhooks-chime-slack-teams/

NEW QUESTION # 70
A DevOps engineer is using AWS CodeDeploy across a fleet of Amazon EC2 instances in an EC2 Auto Scaling group. The associated CodeDeploy deployment group, which is integrated with EC2 Auto Scaling, is configured to perform in-place deployments with codeDeployDefault.oneAtATime During an ongoing new deployment, the engineer discovers that, although the overall deployment finished successfully, two out of five instances have the previous application revision deployed. The other three instances have the newest application revision What is likely causing this issue?

A. A failed Afterinstall lifecycle event hook caused the CodeDeploy agent to roll back to the previous version on the affected instances
B. The two affected instances failed to fetch the new deployment.
C. EC2 Auto Scaling launched two new instances while the new deployment had not yet finished, causing the previous version to be deployed on the affected instances.
D. The CodeDeploy agent was not installed in two affected instances.

Answer: A

Explanation:
Explanation
When AWS CodeDeploy performs an in-place deployment, it updates the instances with the new application revision one at a time, as specified by the deployment configuration codeDeployDefault.oneAtATime. If a lifecycle event hook, such as AfterInstall, fails during the deployment, CodeDeploy will attempt to roll back to the previous version on the affected instances. This is likely what happened with the two instances that still have the previous application revision deployed. The failure of the AfterInstall lifecycle event hook triggered the rollback mechanism, resulting in those instances reverting to the previous application revision.
References:
* AWS CodeDeploy documentation on redeployment and rollback procedures1.
* Stack Overflow discussions on re-deploying older revisions with AWS CodeDeploy2.
* AWS CLI reference guide for deploying a revision2.

NEW QUESTION # 71
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