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Amazon DOP-C02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: High Availability and Disaster Recovery16%- 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
- Implement data backup and restore strategies
  • 1. Design point-in-time recovery solutions
  • 2. Implement cross-region replication
  • 3. Implement validation testing for backups
- Design and implement high availability and scalability
  • 1. Implement load balancing and traffic management
  • 2. Implement auto scaling strategies
  • 3. Design multi-AZ and multi-region architectures
Topic 2: Incident and Event Response18%- Design and implement chaos engineering practices
  • 1. Design resilience testing strategies
  • 2. Implement fault injection experiments (AWS Fault Injection Simulator)
  • 3. Analyze system behavior under failure conditions
- Design and implement event and incident management
  • 1. Implement automated response playbooks
  • 2. Design event aggregation and correlation
  • 3. Implement automated incident detection
Topic 3: Monitoring and Logging12%- 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)
- Design and implement alerting and incident management
  • 1. Design runbook automation
  • 2. Create alarm notification strategies
  • 3. Implement automated incident response
Topic 4: Configuration Management and Infrastructure as Code22%- Design and implement data management strategies
  • 1. Implement data lifecycle management
  • 2. Design backup and recovery solutions
  • 3. Implement database migration strategies
- Design and implement configuration management
  • 1. Implement AWS Systems Manager for configuration management
  • 2. Implement parameter management (AWS Parameter Store, Secrets Manager)
  • 3. Design patch management strategies
- 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
- Implement compliance and configuration monitoring
  • 1. Use AWS Config for compliance monitoring
  • 2. Design remediation automation
  • 3. Implement AWS CloudTrail for auditing
Topic 5: Policies and Standards Automation10%- Design and implement preventive and detective controls
  • 1. Design and implement security baselines
  • 2. Implement drift detection and remediation
  • 3. Implement AWS Organizations and SCPs
- Design and implement governance strategies
  • 1. Implement approval workflows and automation
  • 2. Implement tagging policies and resource grouping
  • 3. Design cost optimization through policies
Topic 6: SDLC Automation22%- Design and implement source code management strategies
  • 1. Determine branching strategies
  • 2. Design code review and approval processes
  • 3. Implement repository configurations and hooks
- Design and implement CI/CD pipelines
  • 1. Develop CI/CD pipelines considering testing and security requirements
  • 2. Implement deployment strategies (blue-green, canary, rolling)
  • 3. Determine appropriate CI/CD pipeline architecture
  • 4. Design failure handling strategies
- Design build and test environments
  • 1. Integrate security scanning and compliance checks
  • 2. Implement build environments (isolated, reproducible)
  • 3. Design test automation frameworks

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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q72-Q77):

NEW QUESTION # 72
A company has developed a static website hosted on an Amazon S3 bucket. The website is deployed using AWS CloudFormation. The CloudFormation template defines an S3 bucket and a custom resource that copies content into the bucket from a source location.
The company has decided that it needs to move the website to a new location, so the existing CloudFormation stack must be deleted and re-created. However, CloudFormation reports that the stack could not be deleted cleanly.
What is the MOST likely cause and how can the DevOps engineer mitigate this problem for this and future versions of the website?

Answer: A

Explanation:
Step 1: Understanding the Deletion FailureThe most likely reason why the CloudFormation stack failed to delete is that the S3 bucket was not empty. AWS CloudFormation cannot delete an S3 bucket that contains objects, so if the website files are still in the bucket, the deletion will fail.
Issue: The S3 bucket is not empty during deletion, preventing the stack from being deleted.
Step 2: Modifying the Custom Resource to Handle DeletionTo mitigate this issue, you can modify the Lambda function associated with the custom resource to automatically empty the S3 bucket when the stack is being deleted. By adding logic to handle the RequestType: Delete event, the function can recursively delete all objects in the bucket before allowing the stack to be deleted.
Action: Modify the Lambda function to recursively delete the objects in the S3 bucket when RequestType is set to Delete.
Why: This ensures that the S3 bucket is empty before CloudFormation tries to delete it, preventing the stack deletion failure.
Reference: AWS documentation on CloudFormation custom resources.
This corresponds to Option B: Deletion has failed because the S3 bucket is not empty. Modify the custom resource's AWS Lambda function code to recursively empty the bucket when RequestType is Delete.


NEW QUESTION # 73
A company is storing 100 GB of log data in csv format in an Amazon S3 bucket SQL developers want to query this data and generate graphs to visualize it. The SQL developers also need an efficient automated way to store metadata from the csv file.
Which combination of steps will meet these requirements with the LEAST amount of effort? (Select THREE.)

Answer: B,C,F

Explanation:
https://docs.aws.amazon.com/glue/latest/dg/components-overview.html


NEW QUESTION # 74
A company has an application that runs on a fleet of Amazon EC2 instances. The application requires frequent restarts. The application logs contain error messages when a restart is required. The application logs are published to a log group in Amazon CloudWatch Logs.
An Amazon CloudWatch alarm notifies an application engineer through an Amazon Simple Notification Service (Amazon SNS) topic when the logs contain a large number of restart-related error messages. The application engineer manually restarts the application on the instances after the application engineer receives a notification from the SNS topic.
A DevOps engineer needs to implement a solution to automate the application restart on the instances without restarting the instances.
Which solution will meet these requirements in the MOST operationally efficient manner?

Answer: A

Explanation:
This solution meets the requirements in the most operationally efficient manner by automating the application restart process on the instances without restarting them. When the CloudWatch alarm enters the ALARM state, the EventBridge rule is triggered, which in turn invokes the Systems Manager Automation runbook that contains the script to restart the application on the instances.


NEW QUESTION # 75
A DevOps engineer notices that all Amazon EC2 instances running behind an Application Load Balancer in an Auto Scaling group are failing to respond to user requests. The EC2 instances are also failing target group HTTP health checks Upon inspection, the engineer notices the application process was not running in any EC2 instances. There are a significant number of out of memory messages in the system logs. The engineer needs to improve the resilience of the application to cope with a potential application memory leak. Monitoring and notifications should be enabled to alert when there is an issue Which combination of actions will meet these requirements? (Select TWO.)

Answer: B,E

Explanation:
https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/metrics-collected-by-CloudWatch-agent.
html


NEW QUESTION # 76
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.)

Answer: B,C,E

Explanation:
Explanation
A comprehensive and detailed explanation is:
* 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 # 77
......

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