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2026 Realistic Amazon DOP-C02 Reliable Braindumps Questions

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The DOP-C02 exam is intended for individuals who have already earned the AWS Certified Developer - Associate or AWS Certified SysOps Administrator - Associate certifications. Candidates should have at least two years of experience in a DevOps role, and should have a deep understanding of AWS services and infrastructure. DOP-C02 Exam consists of 75 multiple-choice and multiple-response questions, and candidates have 180 minutes to complete it.

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Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q368-Q373):

NEW QUESTION # 368
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. 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.
B. 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.
C. 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.
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: C

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 # 369
A company is using the AWS Cloud Development Kit (AWS CDK) to develop a microservices-based application. The company needs to create reusable infrastructure components for three environments:
development, staging, and production. The components must include networking resources, database resources, and serverless compute resources.
The company must implement a solution that provides consistent infrastructure across environments while offering the option for environment-specific customizations. The solution also must minimize code duplication.
Which solution will meet these requirements with the LEAST development overhead?

A. Create custom Level 3 (L3) constructs out of Level 2 (L2) constructs where repeatable patterns exist.
Create a single set of deployment stacks that takes the environment name as an argument upon instantiation. Deploy CDK applications for each environment.
B. Create custom Level 1 (L1) constructs out of Level 2 (L2) constructs where repeatable patterns exist.
Create separate deployment stacks for each environment. Use the CDK context command to determine which stacks to run when deploying to each environment.
C. Create custom Level 1 (L1) constructs out of Level 2 (L2) constructs where repeatable patterns exist.
Create a single set of deployment stacks that takes the environment name as an argument upon instantiation. Deploy CDK applications for each environment.
D. Create custom Level 3 (L3) constructs out of Level 2 (L2) constructs where repeatable patterns exist.Create separate deployment stacks for each environment. Use the CDK context command to determine which stacks to run when deploying to each environment.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of DevOps Engineer Documents Only:
AWS CDK recommends using Level 3 (L3) custom constructs to encapsulate reusable multi-resource infrastructure patterns (networking, compute, DB). Then, define a single set of environment-aware stacks that accept environment parameters for deployment. This ensures consistency with minimal code duplication, per AWS CDK best practices and design patterns whitepaper.

NEW QUESTION # 370
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. 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.
B. 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.
C. 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.
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: C

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.
So D best matches: "validate journeys/endpoints" + "automated detect + rollback" with least operational overhead by relying on managed canaries and alarms rather than custom code/workflows.

NEW QUESTION # 371
A company's application runs on Amazon EC2 instances. The application writes to a log file that records the username, date, time: and source IP address of the login. The log is published to a log group in Amazon CloudWatch Logs The company is performing a root cause analysis for an event that occurred on the previous day The company needs to know the number of logins for a specific user from the past 7 days Which solution will provide this information'?

A. Create a CloudWatch Logs Insights query that uses an aggregation function to count the number of logins for the username over the past 7 days. Run the query against the log group
B. Create a CloudWatch dashboard. Add a number widget that has a filter pattern that counts the number of logins for the username over the past 7 days directly from the log group
C. Create a CloudWatch Logs metric filter on the log group Use a filter pattern that matches the username.
Publish a CloudWatch metric that sums the number of logins over the past 7 days.
D. Create a CloudWatch Logs subscription on the log group Use a filter pattern that matches the username Publish a CloudWatch metric that sums the number of logins over the past 7 days

Answer: A

Explanation:
To analyze and find the number of logins for a specific user from the past 7 days, a CloudWatch Logs Insights query is the most suitable solution. CloudWatch Logs Insights enables you to interactively search and analyze your log data in Amazon CloudWatch Logs. You can use the query language to perform queries that contain multiple commands, including aggregation functions, which can count the occurrences of logins for a specific username over a specified time period. This approach is more direct and efficient than creating a metric filter or subscription, which would require additional steps to publish and sum a metric. References: AWS Certified DevOps Engineer - Professional, CloudWatch Logs Insights query syntax, Tutorial: Run a query with an aggregation function, Add or remove a number widget from a CloudWatch dashboard.

NEW QUESTION # 372
A DevOps engineer has developed an AWS Lambda function The Lambda function starts an AWS CloudFormation drift detection operation on all supported resources for a specific CloudFormation stack The Lambda function then exits Its invocation The DevOps engineer has created an Amazon EventBrdge scheduled rule that Invokes the Lambda function every hour. An Amazon Simple Notification Service (Amazon SNS) topic already exists In the AWS account. The DevOps engineer has subscribed to the SNS topic to receive notifications The DevOps engineer needs to receive a notification as soon as possible when drift is detected in this specific stack configuration.
Which solution Will meet these requirements?

A. Configure the existing EventBridge rule to also target the SNS topic Configure an SNS subscription filter policy to match the Cloud Formation stack. Attach the subscription filter policy to the SNS tomc
B. Configure Amazon GuardDuty in the account with drift detection for all CloudFormation stacks. Create a second EventBndge rule that reacts to the GuardDuty drift detection event finding for the specific CloudFormation stack. Configure the SNS topic as a target of the second EventBridge rule.
C. Configure AWS Config in the account. Use the cloudformation-stack-drift-detection-check managed rule. Create a second EventBndge rule that reacts to a compliance change event for the CloudFormaUon stack. Configure the SNS topc as a target of the second EventBridge rule.
D. Create a second Lambda function to query the CloudFormation API for the drift detection results for the stack Configure the second Lambda function to publish a message to the SNS topic If drift ts detected Adjust the existing EventBridge rule to also target the second Lambda function

Answer: C

Explanation:
A comprehensive and detailed explanation is:
Option A is incorrect because EventBridge rules cannot filter events based on the message body or attributes of the target service. Therefore, configuring an SNS subscription filter policy to match the CloudFormation stack will not work. The SNS topic will receive all events from the EventBridge rule, regardless of the stack name or drift status.
Option B is incorrect because it introduces unnecessary complexity and cost. Creating a second Lambda function to query the CloudFormation API for the drift detection results is redundant, since CloudFormation already publishes drift detection events to EventBridge. Moreover, invoking two Lambda functions every hour will incur more charges than invoking one.
Option C is incorrect because GuardDuty does not provide drift detection for CloudFormation stacks. GuardDuty is a threat detection service that monitors for malicious activity and unauthorized behavior in AWS accounts and workloads. It does not monitor or report on configuration changes or drifts in CloudFormation stacks.
Option D is correct because it leverages AWS Config and its managed rule for drift detection. AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources. It can detect configuration changes and drifts in CloudFormation stacks using the cloudformation-stack-drift-detection-check managed rule. This rule triggers an AWS Config event when a stack drifts from its expected template configuration. By creating a second EventBridge rule that reacts to this event for the specific stack, the DevOps engineer can configure the SNS topic as a target and receive a notification as soon as possible when drift is detected.
Reference:
AWS Config
Amazon SNS subscription filter policies
Amazon EventBridge rules

NEW QUESTION # 373
......

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