The AWS Certified DevOps Engineer - Professional Exam (DOP-C02) validates advanced skills in automating infrastructure, managing configurations, and operating resilient cloud solutions on Amazon Web Services. This certification is designed for experienced DevOps professionals who architect and implement strategies across the full software development lifecycle. This landing page guides you through the exam structure, core topics, and effective preparation strategies to help you succeed on your first attempt.
Use this topic map to guide your study for Amazon DOP-C02 (AWS Certified DevOps Engineer - Professional Exam) within the Amazon Professional path.
The DOP-C02 exam measures both theoretical knowledge and practical decision-making through a variety of question types designed to reflect real-world DevOps challenges.
Questions progress in difficulty and emphasize practical application over memorization, rewarding candidates who have hands-on experience with AWS DevOps tools and patterns.
Effective preparation requires a structured approach that maps exam topics to weekly study goals and reinforces learning through practice and review. Allocate time based on topic weight and your current skill gaps, then validate your understanding with realistic practice scenarios.
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SDLC Automation and Configuration Management and IaC typically account for a significant portion of the exam, as they form the foundation of DevOps practice. However, all six domains are tested, so balanced preparation across all topics is essential. Focus extra attention on areas where you have less hands-on experience.
In practice, automated pipelines deploy infrastructure code to environments, and monitoring systems detect issues that trigger incident response workflows. Understanding these connections helps you design end-to-end solutions rather than treating each topic in isolation. The exam rewards candidates who see DevOps as an integrated lifecycle, not separate tools.
AWS recommends at least two years of hands-on experience with AWS services and DevOps practices. Practical experience with CodePipeline, CloudFormation, monitoring, and incident response is especially valuable. If you lack depth in specific areas, labs and practice tests help bridge knowledge gaps, but they cannot fully replace real-world exposure.
Candidates often overlook nuances in deployment strategies, misunderstand the scope of AWS services, or confuse similar features (e.g., CloudFormation vs. Systems Manager OpsCenter). Another frequent error is choosing the cheapest solution without considering availability or compliance requirements. Slow reading of scenario details also leads to selecting answers that address the wrong problem.
Focus on high-weighted topics and revisit your weakest practice test sections. Create a one-page cheat sheet of key definitions, service limits, and decision trees for common scenarios. Avoid cramming new topics; instead, do quick spot checks on familiar material to build confidence. Get adequate sleep and do a final timed practice test three days before the exam.
A company recently launched multiple applications that use Application Load Balancers. Application response time often slows down when the applications experience problems A DevOps engineer needs to Implement a monitoring solution that alerts the company when the applications begin to perform slowly The DevOps engineer creates an Amazon Simple Notification Semce (Amazon SNS) topic and subscribe the company's email address to the topic
What should the DevOps engineer do next to meet the requirements?
Option A is incorrect because creating an Amazon EventBridge rule that invokes an AWS Lambda function to query the applications on a 5-minute interval is not a valid solution. EventBridge rules can only trigger Lambda functions based on events, not on time intervals. Moreover, querying the applications on a 5-minute interval might incur unnecessary costs and network overhead, and might not detect performance issues in real time.
Option B is correct because creating an Amazon CloudWatch Synthetics canary that runs a custom script to query the applications on a 5-minute interval is a valid solution. CloudWatch Synthetics canaries are configurable scripts that monitor endpoints and APIs by simulating customer behavior. Canaries can run as often as once per minute, and can measure the latency and availability of the applications.Canaries can also send notifications to an Amazon SNS topic when they detect errors or performance issues1.
Option C is incorrect because creating an Amazon CloudWatch alarm that uses the AWS/ApplicationELB namespace RequestCountPerTarget metric is not a valid solution.The RequestCountPerTarget metric measures the number of requests completed or connections made per target in a target group2. This metric does not reflect the application response time, which is the requirement. Moreover, configuring the CloudWatch alarm to send a notification when the number of connections becomes greater than the configured number of threads that the application supports is not a valid way to measure the application performance, as it depends on the application design and implementation.
Option D is incorrect because creating an Amazon CloudWatch alarm that uses the AWS/ApplicationELB namespace RequestCountPerTarget metric is not a valid solution, for the same reason as option C. The RequestCountPerTarget metric does not reflect the application response time, which is the requirement. Moreover, configuring the CloudWatch alarm to send a notification when the average response time becomes greater than the longest response time that the application supports is not a valid way to measure the application performance, as it does not account for variability or outliers in the response time distribution.
References:
1:Using synthetic monitoring
2:Application Load Balancer metrics
A software team is using AWS CodePipeline to automate its Java application release pipeline The pipeline consists of a source stage, then a build stage, and then a deploy stage. Each stage contains a single action that has a runOrder value of 1.
The team wants to integrate unit tests into the existing release pipeline. The team needs a solution that deploys only the code changes that pass all unit tests.
Which solution will meet these requirements?
Modify the Build Stage to Add a Test Action with a RunOrder Value of 2:
The build stage in AWS CodePipeline can have multiple actions. By adding a test action with a runOrder value of 2, the test action will execute after the initial build action completes.
Use AWS CodeBuild as the Action Provider to Run Unit Tests:
AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages.
Using CodeBuild to run unit tests ensures that the tests are executed in a controlled environment and that only the code changes that pass the unit tests proceed to the deploy stage.
Example configuration in CodePipeline:
{
'name': 'BuildStage',
'actions': [
{
'name': 'Build',
'actionTypeId': {
'category': 'Build',
'owner': 'AWS',
'provider': 'CodeBuild',
'version': '1'
},
'runOrder': 1
},
{
'name': 'Test',
'actionTypeId': {
'category': 'Test',
'owner': 'AWS',
'provider': 'CodeBuild',
'version': '1'
},
'runOrder': 2
}
]
}
By integrating the unit tests into the build stage and ensuring they run after the build process, the pipeline guarantees that only code changes passing all unit tests are deployed.
References:
AWS CodePipeline
AWS CodeBuild
Using CodeBuild with CodePipeline
A company uses a pipeline in AWS CodePipeline to upload AWS CloudFormation templates to an Amazon S3 bucket. The pipeline uses the templates to deploy CloudFormation stacks that match the names of the templates.
The company has experienced issues when it tries to revert templates to a previous version. To prevent these issues, the company must have the ability to review template modifications before the modifications are deployed to production.
Which solution will meet these requirements with the LEAST operational overhead?
The requirement is simply: review changes before production deployment, with the least operational overhead.
B is the lightest change: adding a Manual approval (review) action in CodePipeline creates a controlled gate before the deploy stage. It requires no new repositories, no new services, and no custom code---just pipeline configuration.
Why not the others:
A introduces additional moving parts (Git repo integration, PR workflow management, and CloudFormation Git sync). That's useful, but it's more operational overhead than necessary to satisfy ''review before deploy.''
C requires custom Lambda logic to inspect templates and decide whether to proceed---more code to write, run, secure, and maintain.
D adds both Git integration and a manual approval step---again more overhead than just adding the approval gate.
A company is using AWS CodeDeploy to automate software deployment. The deployment must meet these requirements:
* A number of instances must be available to serve traffic during the deployment Traffic must be balanced across those instances, and the instances must automatically heal in the event of failure.
* A new fleet of instances must be launched for deploying a new revision automatically, with no manual provisioning.
* Traffic must be rerouted to the new environment to half of the new instances at a time. The deployment should succeed if traffic is rerouted to at least half of the instances; otherwise, it should fail.
* Before routing traffic to the new fleet of instances, the temporary files generated during the deployment process must be deleted.
* At the end of a successful deployment, the original instances in the deployment group must be deleted immediately to reduce costs.
How can a DevOps engineer meet these requirements?
Step 1: Use a Blue/Green Deployment StrategyA blue/green deployment strategy is necessary to meet the requirement of launching a new fleet of instances for each deployment and ensuring availability. In a blue/green deployment, the new version (green environment) is deployed to a separate set of instances, while the old version (blue environment) remains active. After testing the new version, traffic can be gradually shifted.
Action: Use AWS CodeDeploy's blue/green deployment configuration.
Why: Blue/green deployment minimizes downtime and ensures that traffic is shifted only to healthy instances.
Step 2: Use an Application Load Balancer and Auto Scaling GroupThe Application Load Balancer (ALB) is essential to balance traffic across multiple instances, and Auto Scaling ensures the deployment scales automatically to meet demand.
Action: Associate the Auto Scaling group and Application Load Balancer target group with the deployment group.
Why: This configuration ensures that traffic is evenly distributed and that instances automatically scale based on traffic load.
Step 3: Use Custom Deployment ConfigurationThe company requires that traffic be rerouted to at least half of the instances to succeed. AWS CodeDeploy allows you to configure custom deployment settings with specific thresholds for healthy hosts.
Action: Create a custom deployment configuration where 50% of the instances must be healthy.
Why: This ensures that the deployment continues only if at least 50% of the new instances are healthy.
Step 4: Clean Temporary Files Using HooksBefore routing traffic to the new environment, the temporary files generated during the deployment must be deleted. This can be achieved using the BeforeAllowTraffic hook in the appspec.yml file.
Action: Use the BeforeAllowTraffic lifecycle event hook to clean up temporary files before routing traffic to the new environment.
Why: This ensures that the environment is clean before the new instances start serving traffic.
Step 5: Terminate Original Instances After DeploymentAfter a successful deployment, AWS CodeDeploy can automatically terminate the original instances (blue environment) to save costs.
Action: Instruct AWS CodeDeploy to terminate the original instances after the new instances are healthy.
Why: This helps in cost reduction by removing unused instances after the deployment.
This corresponds to Option C: Use an Application Load Balancer and a blue/green deployment. Associate the Auto Scaling group and the Application Load Balancer target group with the deployment group. Use the Automatically copy Auto Scaling group option, and use CodeDeployDefault.HalfAtATime as the deployment configuration. Instruct AWS CodeDeploy to terminate the original instances in the deployment group, and use the BeforeAllowTraffic hook within appspec.yml to delete the temporary files.
A company has multiple development groups working in a single shared AWS account. The Senior Manager of the groups wants to be alerted via a third-party API call when the creation of resources approaches the service limits for the account.
Which solution will accomplish this with the LEAST amount of development effort?
To meet the requirements, the company needs to create a solution that alerts the Senior Manager when the creation of resources approaches the service limits for the account with the least amount of development effort. The company can use AWS Trusted Advisor, which is a service that provides best practice recommendations for cost optimization, performance, security, and service limits. The company can deploy an AWS Lambda function that refreshes Trusted Advisor checks, and configure an Amazon CloudWatch Events rule to run the Lambda function periodically. This will ensure that Trusted Advisor checks are up to date and reflect the current state of the account. The company can then create another CloudWatch Events rule with an event pattern matching Trusted Advisor events and a target Lambda function. The event pattern can filter for events related to service limit checks and their status. The target Lambda function can notify the Senior Manager via a third-party API call if the event indicates that the account is approaching or exceeding a service limit.
