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Amazon DOP-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Incident and Event Response	14%	- Automate event management 1. Integrate with ticketing and workflow systems 2. Automate common operational tasks 3. Classify and prioritize events - Improve operational processes 1. Implement feedback loops 2. Optimize response time and efficiency 3. Update runbooks and playbooks - Manage incidents and outages 1. Coordinate response and recovery 2. Document and learn from events 3. Troubleshoot complex issues
Topic 2: SDLC Automation	22%	- Automate testing and validation 1. Unit, integration, and performance testing 2. Automate quality gates 3. Security and compliance testing - Implement CI/CD pipelines 1. Automate build, test, and deployment stages 2. Integrate version control systems 3. Use AWS CodePipeline, CodeBuild, CodeDeploy - Automate software release processes 1. Implement deployment strategies 2. Manage artifacts and dependencies 3. Automate rollback procedures
Topic 3: Security, Governance, and Compliance	17%	- Enforce governance and compliance 1. Implement compliance rules and audits 2. Use AWS Config and AWS Organizations 3. Manage access and permissions at scale - Implement security controls 1. Secure secrets and credentials 2. Encrypt data at rest and in transit 3. Manage IAM for pipelines and environments - Automate security and compliance 1. Detect and remediate violations automatically 2. Integrate security into CI/CD pipelines 3. Use AWS Security Hub and AWS Shield
Topic 4: Configuration Management and Infrastructure as Code	17%	- Automate configuration management 1. Use AWS Systems Manager, Ansible, or Puppet 2. Maintain consistent configurations 3. Detect and remediate configuration drift - Implement infrastructure as code 1. Enforce configuration standards 2. Manage templates and stacks 3. Use AWS CloudFormation, Terraform, or AWS CDK - Manage infrastructure deployments 1. Implement version control for configurations 2. Provision and update resources automatically 3. Manage multi-account and multi-region environments
Topic 5: Resilient Cloud Solutions	15%	- Design high availability architectures 1. Use Route 53 for routing and failover 2. Multi-AZ and multi-region deployments 3. Implement auto scaling and load balancing - Implement disaster recovery 1. Backup and restore strategies 2. Test and validate recovery plans 3. Pilot light, warm standby, and active-active - Ensure reliability and scalability 1. Manage capacity and elasticity 2. Optimize performance and cost 3. Design for fault tolerance
Topic 6: Monitoring and Logging	15%	- Automate incident response 1. Automate remediation and scaling 2. Set up event-driven actions with Amazon EventBridge 3. Implement alerting and notification workflows - Implement monitoring solutions 1. Use Amazon CloudWatch for metrics and alarms 2. Monitor infrastructure, applications, and costs 3. Create dashboards and insights - Implement logging and analysis 1. Analyze logs for troubleshooting and security 2. Centralize logs with CloudWatch Logs 3. Use AWS X-Ray for tracing

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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q62-Q67):

NEW QUESTION # 62
A company has implemented a new microservices-based application on an Amazon Elastic Container Service (Amazon ECS) cluster. After each deployment, the company wants to validate the critical user journeys and API endpoints before routing traffic to the new application version.
The company must implement an automated solution to detect issues in the new deployment and to initiate a rollback if necessary.
Which solution will meet these requirements with the LEAST operational overhead?

A. Set up Amazon CloudWatch Application Insights for the ECS cluster. Configure Application Insights to monitor key performance indicators of the microservices in the critical user journeys and API calls. Create CloudWatch alarms based on the insights. Use EventBridge to invoke an AWS Step Functions workflow to evaluate the alarms. Configure the workflow to initiate a rollback if necessary by using the alarms' built-in integration with Amazon ECS.
B. Create CloudWatch Synthetics canaries that simulate critical user journeys and API calls. Configure the canaries to run against the new deployment. Create CloudWatch alarms that are invoked when canaries fail. Use the alarms' built-in integration with Amazon ECS to initiate a rollback if the alarms are invoked before traffic is routed to the new deployment.
C. Set up Amazon CloudWatch Application Insights for the ECS cluster. Create an Amazon EventBridge rule to invoke an AWS Lambda function to analyze the task states. Program the Lambda function to use the ECS UpdateService API call to initiate a rollback if a specific percentage of tasks fail.
D. Create CloudWatch Synthetics canaries that simulate critical user journeys and API calls. Implement AWS X-Ray tracing for all the microservices. Configure X-Ray to send traces to CloudWatch. Create CloudWatch alarms based on error rates and latency metrics. Create a Lambda function to analyze the traces and to initiate a rollback if necessary by using the alarms' built-in integration with Amazon ECS.

Answer: B

Explanation:
The requirement is specifically to validate critical user journeys and API endpoints after each deployment and before routing traffic. The most direct AWS-native way to test "real" user flows and endpoints automatically is CloudWatch Synthetics canaries, which can run scripted checks (HTTP/API and browser-style journeys).
Option D minimizes operational overhead because it uses a straightforward managed pattern:
Canaries run against the new deployment to validate endpoints and journeys.
CloudWatch alarms trigger when the canaries fail (objective signal of broken journeys/APIs).
The solution then uses alarm-driven rollback (the intent here is automated rollback without writing custom analysis code or managing multi-service workflows).
Why the other options have more overhead or miss the "user journey/API validation" goal as cleanly:
A focuses on ECS task states (healthy/unhealthy tasks). That can detect deployment failures, but it doesn't validate critical user journeys or API correctness. Also requires custom Lambda logic for analysis + rollback control.
B adds even more moving parts (Application Insights + alarms + EventBridge + Step Functions). Also, monitoring KPIs is not the same as explicitly validating journeys/endpoints before routing traffic.
C is the highest overhead: canaries plus X-Ray instrumentation across all microservices, trace exporting, custom Lambda analysis, etc. That's heavy compared to canary + alarm.

NEW QUESTION # 63
A company is adopting AWS CodeDeploy to automate its application deployments for a Java-Apache Tomcat application with an Apache Webserver. The development team started with a proof of concept, created a deployment group for a developer environment, and performed functional tests within the application. After completion, the team will create additional deployment groups for staging and production.
The current log level is configured within the Apache settings, but the team wants to change this configuration dynamically when the deployment occurs, so that they can set different log level configurations depending on the deployment group without having a different application revision for each group.
How can these requirements be met with the LEAST management overhead and without requiring different script versions for each deployment group?

A. Create a CodeDeploy custom environment variable for each environment. Then place a script into the application revision that checks this environment variable to identify which deployment group the instance is part of. Use this information to configure the log level settings. Reference this script as part of the ValidateService lifecycle hook in the appspec.yml file.
B. Create a script that uses the CodeDeploy environment variable DEPLOYMENT_GROUP_ NAME to identify which deployment group the instance is part of. Use this information to configure the log level settings. Reference this script as part of the BeforeInstall lifecycle hook in the appspec.yml file.
C. Tag the Amazon EC2 instances depending on the deployment group. Then place a script into the application revision that calls the metadata service and the EC2 API to identify which deployment group the instance is part of. Use this information to configure the log level settings. Reference the script as part of the AfterInstall lifecycle hook in the appspec.yml file.
D. Create a script that uses the CodeDeploy environment variable DEPLOYMENT_GROUP_ID to identify which deployment group the instance is part of to configure the log level settings. Reference this script as part of the Install lifecycle hook in the appspec.yml file.

Answer: B

Explanation:
Explanation
The following are the steps that the company can take to change the log level dynamically when the deployment occurs:
Create a script that uses the CodeDeploy environment variable DEPLOYMENT_GROUP_NAME to identify which deployment group the instance is part of.
Use this information to configure the log level settings.
Reference this script as part of the BeforeInstall lifecycle hook in the appspec.yml file.
The DEPLOYMENT_GROUP_NAME environment variable is automatically set by CodeDeploy when the deployment is triggered. This means that the script does not need to call the metadata service or the EC2 API to identify the deployment group.
This solution is the least complex and requires the least management overhead. It also does not require different script versions for each deployment group.
The following are the reasons why the other options are not correct:
Option A is incorrect because it would require tagging the Amazon EC2 instances, which would be a manual and time-consuming process.
Option C is incorrect because it would require creating a custom environment variable for each environment. This would be a complex and error-prone process.
Option D is incorrect because it would use the DEPLOYMENT_GROUP_ID environment variable.
However, this variable is not automatically set by CodeDeploy, so the script would need to call the metadata service or the EC2 API to get the deployment group ID. This would add complexity and overhead to the solution.

NEW QUESTION # 64
A development team manually builds a local artifact. The development team moves the artifact to an Amazon S3 bucket to support an application. The application has a local cache that must be cleared when the development team deploys the application to Amazon EC2 instances. For each deployment, the development team runs a command to clear the cache, download the artifact from the S3 bucket, and unzip the artifact to complete the deployment.
The development team wants to migrate the deployment process to a CI/CD process and to track the progress of each deployment.
Which combination of actions will meet these requirements with the MOST operational efficiency? (Select THREE.)

A. Create a custom script to clear the cache. Specify the script in the BeforeInstall lifecycle hook in the AppSpec file.
B. Set up an AWS CodeConnections compatible Git repository. Allow developers to merge code into the repository. Use AWS CodeBuild to build an artifact and copy the object into the S3 bucket. Configure CodeBuild to run for every merge into the main branch.
C. Create user data for each EC2 instance that contains the cache clearing script. Test the application after deployment. If the deployment is not successful, then redeploy.
D. Use AWS CodePipeline to deploy the application. Set up an AWS CodeConnections compatible Git repository. Allow developers to merge code into the repository as a source for the pipeline.
E. Use AWS CodeBuild to build the artifact and place the artifact in the S3 bucket. Use AWS CodeDeploy to deploy the artifact to EC2 instances.
F. Use AWS Systems Manager to fetch the artifact from the S3 bucket and to deploy the artifact to all the EC2 instances.

Answer: A,D,E

Explanation:
Use CodePipeline with a Git source to orchestrate the flow, CodeBuild to produce versioned artifacts in S3, and CodeDeploy to EC2 with lifecycle hooks (e.g., BeforeInstall) to run cache-clearing and unpack steps.
This gives end-to-end visibility, rollbacks, and standardized deployments.

NEW QUESTION # 65
A company needs to adopt a multi-account strategy to deploy its applications and the associated CI/CD infrastructure. The company has created an organization in AWS Organizations that has all features enabled.
The company has configured AWS Control Tower and has set up a landing zone.
The company needs to use AWS Control Tower controls (guardrails) in all AWS accounts in the organization.
The company must create the accounts for a multi-environment application and must ensure that all accounts are configured to an initial baseline.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Organizations to provision a dedicated AWS account for each environment, an audit account, and a CI/CD account. Use AWS CloudFormation StackSets to apply the baseline configuration to the new accounts.
B. Create an AWS Control Tower Account Factory Customization (AFC) blueprint that uses the baseline configuration. Use AWS Control Tower Account Factory to provision a dedicated AWS account for each environment and a CI/CD account by using the blueprint.
C. Use AWS Control Tower Account Factory to provision a dedicated AWS account for each environment and a CI/CD account. Use AWS CloudFormation StackSets to apply the baseline configuration to the new accounts.
D. Use Organizations to provision a multi-environment AWS account and a CI/CD account. In the Organizations management account, create an AWS Lambda function that assumes the Organizations access role to apply the baseline configuration to the new accounts.

Answer: B

Explanation:
Comprehensive & Detailed Explanation (150-250 words):
AWS Control Tower provides Account Factory and Account Factory Customizations (AFC) as the lowest- overhead and most scalable method for creating and configuring new accounts. AFC allows you to define blueprints that include mandatory baseline resources such as IAM roles, logging configurations, guardrails, network settings, and tagging standards. When accounts are created through Account Factory using a customization blueprint, all controls and baseline configurations are applied automatically during provisioning, without any additional automation steps or StackSet deployments.
Option B requires running StackSets after the account is created, which adds manual management overhead and defeats the built-in automation advantages of Control Tower. Options C and D rely on manually provisioning accounts via AWS Organizations, which bypasses Control Tower's governance and would require custom Lambda or StackSet automation to replicate controls that Control Tower already provides automatically.
Because the question explicitly asks for the least operational overhead, the correct answer is to use the Control Tower AFC blueprint, ensuring every account is born compliant with the required guardrails and baseline configuration.

NEW QUESTION # 66
A DevOps engineer manages a large commercial website that runs on Amazon EC2. The website uses Amazon Kinesis Data Streams to collect and process web togs. The DevOps engineer manages the Kinesis consumer application, which also runs on Amazon EC2.
Sudden increases of data cause the Kinesis consumer application to (all behind and the Kinesis data streams drop records before the records can be processed. The DevOps engineer must implement a solution to improve stream handling.
Which solution meets these requirements with the MOST operational efficiency?

A. Increase the number of shards in the Kinesis data streams to increase the overall throughput so that the consumer application processes the data faster.
B. Convert the Kinesis consumer application to run as an AWS Lambda function. Configure the Kinesis data streams as the event source for the Lambda function to process the data streams
C. Horizontally scale the Kinesis consumer application by adding more EC2 instances based on the Amazon CloudWatch GetRecords IteratorAgeMilliseconds metric Increase the retention period of the Kinesis data streams.
D. Modify the Kinesis consumer application to store the logs durably in Amazon S3 Use Amazon EMR to process the data directly on Amazon S3 to derive customer insights Store the results in Amazon S3.

Answer: C

Explanation:
Explanation
https://docs.aws.amazon.com/streams/latest/dev/monitoring-with-cloudwatch.html GetRecords.IteratorAgeMilliseconds - The age of the last record in all GetRecords calls made against a Kinesis stream, measured over the specified time period. Age is the difference between the current time and when the last record of the GetRecords call was written to the stream. The Minimum and Maximum statistics can be used to track the progress of Kinesis consumer applications. A value of zero indicates that the records being read are completely caught up.

NEW QUESTION # 67
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