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Amazon DOP-C02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: SDLC Automation22%- Automate software release processes
  • 1. Manage artifacts and dependencies
  • 2. Automate rollback procedures
  • 3. Implement deployment strategies
- Automate testing and validation
  • 1. Security and compliance testing
  • 2. Automate quality gates
  • 3. Unit, integration, and performance testing
- Implement CI/CD pipelines
  • 1. Integrate version control systems
  • 2. Use AWS CodePipeline, CodeBuild, CodeDeploy
  • 3. Automate build, test, and deployment stages
Topic 2: Security, Governance, and Compliance17%- Implement security controls
  • 1. Encrypt data at rest and in transit
  • 2. Manage IAM for pipelines and environments
  • 3. Secure secrets and credentials
- Automate security and compliance
  • 1. Integrate security into CI/CD pipelines
  • 2. Use AWS Security Hub and AWS Shield
  • 3. Detect and remediate violations automatically
- Enforce governance and compliance
  • 1. Use AWS Config and AWS Organizations
  • 2. Implement compliance rules and audits
  • 3. Manage access and permissions at scale
Topic 3: Configuration Management and Infrastructure as Code17%- Implement infrastructure as code
  • 1. Enforce configuration standards
  • 2. Use AWS CloudFormation, Terraform, or AWS CDK
  • 3. Manage templates and stacks
- Automate configuration management
  • 1. Maintain consistent configurations
  • 2. Detect and remediate configuration drift
  • 3. Use AWS Systems Manager, Ansible, or Puppet
- Manage infrastructure deployments
  • 1. Manage multi-account and multi-region environments
  • 2. Implement version control for configurations
  • 3. Provision and update resources automatically
Topic 4: Incident and Event Response14%- Improve operational processes
  • 1. Optimize response time and efficiency
  • 2. Implement feedback loops
  • 3. Update runbooks and playbooks
- Automate event management
  • 1. Classify and prioritize events
  • 2. Integrate with ticketing and workflow systems
  • 3. Automate common operational tasks
- Manage incidents and outages
  • 1. Coordinate response and recovery
  • 2. Troubleshoot complex issues
  • 3. Document and learn from events
Topic 5: Monitoring and Logging15%- Implement logging and analysis
  • 1. Centralize logs with CloudWatch Logs
  • 2. Use AWS X-Ray for tracing
  • 3. Analyze logs for troubleshooting and security
- Automate incident response
  • 1. Implement alerting and notification workflows
  • 2. Set up event-driven actions with Amazon EventBridge
  • 3. Automate remediation and scaling
- Implement monitoring solutions
  • 1. Use Amazon CloudWatch for metrics and alarms
  • 2. Create dashboards and insights
  • 3. Monitor infrastructure, applications, and costs
Topic 6: Resilient Cloud Solutions15%- Ensure reliability and scalability
  • 1. Optimize performance and cost
  • 2. Manage capacity and elasticity
  • 3. Design for fault tolerance
- Design high availability architectures
  • 1. Implement auto scaling and load balancing
  • 2. Multi-AZ and multi-region deployments
  • 3. Use Route 53 for routing and failover
- Implement disaster recovery
  • 1. Pilot light, warm standby, and active-active
  • 2. Backup and restore strategies
  • 3. Test and validate recovery plans

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Amazon AWS Certified DevOps Engineer - Professional 認定 DOP-C02 試験問題 (Q350-Q355):

質問 # 350
A DevOps team is deploying microservices for an application on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The cluster uses managed node groups.
The DevOps team wants to enable auto scaling for the microservice Pods based on a specific CPU utilization percentage. The DevOps team has already installed the Kubernetes Metrics Server on the cluster.
Which solution will meet these requirements in the MOST operationally efficient way?

正解：B

解説：
To scale microservice Pods based on CPU utilization, the Kubernetes Horizontal Pod Autoscaler (HPA) uses the Kubernetes Metrics Server to monitor resource usage and automatically adjusts the number of Pods.
However, scaling Pods may require additional nodes if the current node capacity is insufficient.
* The Cluster Autoscaler works with EKS managed node groups to add or remove worker nodes based on pending Pod requirements and resource usage.
* By deploying both HPA and Cluster Autoscaler, the system can automatically scale Pods and add nodes as necessary, ensuring efficient resource utilization and availability.
* Configuring the Cluster Autoscaler with auto-discovery allows it to manage node groups without manual intervention, reducing operational effort.
* Option A only scales nodes based on node CPU utilization, not Pods.
* Option B uses VPA recommender mode, which only suggests resource changes and does not scale automatically.
* Option C involves manual updates and is not automated scaling. Therefore, option D provides the most operationally efficient, fully automated scaling solution.
Reference from AWS Official Documentation:
* Kubernetes Horizontal Pod Autoscaler: " HPA automatically scales the number of Pods based on observed CPU utilization or other metrics. " (Kubernetes HPA)
* Cluster Autoscaler on Amazon EKS: " The Cluster Autoscaler automatically adjusts the size of the Kubernetes cluster when there are Pods that fail to run due to insufficient resources or when nodes in the cluster are underutilized. " (AWS EKS Cluster Autoscaler)


質問 # 351
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?

正解：B

解説：
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.


質問 # 352
A company has developed a serverless web application that is hosted on AWS. The application consists of Amazon S3. Amazon API Gateway, several AWS Lambda functions, and an Amazon RDS for MySQL database. The company is using AWS CodeCommit to store the source code. The source code is a combination of AWS Serverless Application Model (AWS SAM) templates and Python code.
A security audit and penetration test reveal that user names and passwords for authentication to the database are hardcoded within CodeCommit repositories. A DevOps engineer must implement a solution to automatically detect and prevent hardcoded secrets.
What is the MOST secure solution that meets these requirements?

正解：B

解説：
Explanation
https://docs.aws.amazon.com/codecommit/latest/userguide/how-to-amazon-codeguru-reviewer.html


質問 # 353
A company uses Amazon S3 to store proprietary information. The development team creates buckets for new projects on a daily basis. The security team wants to ensure that all existing and future buckets have encryption logging and versioning enabled. Additionally, no buckets should ever be publicly read or write accessible.
What should a DevOps engineer do to meet these requirements?

正解：B

解説：
https://aws.amazon.com/blogs/mt/aws-config-auto-remediation-s3-compliance/ https://aws.amazon.com/blogs/aws/aws-config-rules-dynamic-compliance-checking-for-cloud-resources/


質問 # 354
A company has deployed an application in a single AWS Region. The application backend uses Amazon DynamoDB tables and Amazon S3 buckets.
The company wants to deploy the application in a secondary Region. The company must ensure that the data in the DynamoDB tables and the S3 buckets persists across both Regions. The data must also immediately propagate across Regions.
Which solution will meet these requirements with the MOST operational efficiency?

正解：C

解説：
The company needs multi-Region data persistence with immediate propagation and minimal operational overhead. For S3, the correct mechanism is S3 replication (Cross-Region Replication or Same-Region Replication), which continuously and asynchronously replicates new objects as they are written. Configuring two-way replication between the primary and secondary Region buckets ensures that objects written in either Region are replicated to the other automatically without custom code.
For DynamoDB, the native solution for multi-Region replication is DynamoDB global tables. Global tables provide multi-master, multi-Region replication with low-latency reads and writes in each Region and automatic propagation of changes. Converting existing tables into global tables and adding the secondary Region as a replica gives immediate, managed cross-Region replication with minimal maintenance.
Option A combines these two fully managed features: two-way S3 replication and DynamoDB global tables.
This yields the highest operational efficiency.
Options B, C, and D rely on S3 Batch Operations or DynamoDB Streams + Lambda to manually copy data cross-Region. These approaches add complexity, custom code, and operational risk, and they are less suitable when AWS provides managed replication mechanisms specifically designed for this purpose.
Therefore, Option A is the correct and most efficient solution.


質問 # 355
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