
AWS DOP-C02 Exam Practice Questions
US$39
Every question with the correct answer and why the other options are wrong.
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Honestly, I was a bit skeptical about Certstash at first, but these DOP-C02 dumps were a total game-changer. I saw at least 80% of these exact questions on the real exam. Passed this morning with a great score. Seriously, don't hesitate.
Three of the 480 questions in this pack
Question 1
A company has a mobile application that makes HTTP API calls to an Application Load Balancer (ALB). The ALB routes requests to an AWS Lambda function. Many different versions of the application are in use at any given time, including versions that are in testing by a subset of users. The version of the application is defined in the user-agent header that is sent with all requests to the API. After a series of recent changes to the API, the company has observed issues with the application. The company needs to gather a metric for each API operation by response code for each version of the application that is in use. A DevOps engineer has modified the Lambda function to extract the API operation name, version information from the user-agent header and response code. Which additional set of actions should the DevOps engineer take to gather the required metrics?
Show answer and explanation
Correct answer: A. Modify the Lambda function to write the API operation name, response code, and version number as a log line to an Amazon CloudWatch Logs log group. Configure a CloudWatch Logs metric filter that increments a metric for each API operation name. Specify response code and application version as dimensions for the metric.
The solution requires extracting the API operation name, response code, and application version from the Lambda function and creating custom metrics with these dimensions. Option A correctly identifies that CloudWatch Logs metric filters can parse log lines and create metrics with multiple dimensions (response code and application version) for each API operation. This is the standard, native approach for converting log data into metrics. Option B suggests using CloudWatch Logs Insights, which is a query tool and does not automatically populate metrics; it requires manual queries. Option C incorrectly focuses on ALB access logs and response metadata, which would not contain the API operation details. Option D uses X-Ray, which adds complexity and requires additional configuration without the direct metric dimension support needed.
Why the other options are wrong
- B. CloudWatch Logs Insights is a query tool for analyzing logs, not a mechanism for automatically populating CloudWatch metrics.
- C. ALB access logs don't contain API operation details, and response metadata cannot be parsed by ALB to extract this information.
- D. X-Ray adds unnecessary complexity and does not provide the straightforward metric filtering with dimensions that metric filters offer.
Question 2
A company provides an application to customers. The application has an Amazon API Gateway REST API that invokes an AWS Lambda function. On initialization, the Lambda function loads a large amount of data from an Amazon DynamoDB table. The data load process results in long coldstart times of 8-10 seconds. The DynamoDB table has DynamoDB Accelerator (DAX) configured. Customers report that the application intermittently takes a long time to respond to requests. The application receives thousands of requests throughout the day. In the middle of the day, the application experiences 10 times more requests than at any other time of the day. Near the end of the day, the application's request volume decreases to 10% of its normal total. A DevOps engineer needs to reduce the latency of the Lambda function at all times of the day. Which solution will meet these requirements?
Show answer and explanation
Correct answer: C. Configure provisioned concurrency on the Lambda function. Configure AWS Application Auto Scaling on the Lambda function with provisioned concurrency values set to a minimum of 1 and a maximum of 100.
The problem involves variable traffic patterns with peak demand 10 times normal and end-of-day valleys at 10% of normal, combined with 8-10 second cold start times. Provisioned concurrency eliminates cold starts by maintaining pre-initialized Lambda instances, and Application Auto Scaling allows the provisioned concurrency to scale dynamically based on demand. Setting a minimum of 1 ensures some warm instances always exist to handle baseline traffic, while a maximum of 100 accommodates the 10x peak demand periods. This directly addresses the cold start latency issue and scales cost-effectively with traffic variations. Option A sets concurrency to only 1, which cannot handle peak traffic. Option B uses reserved concurrency with value 0, which doesn't reduce cold starts. Option D applies reserved concurrency and scales API Gateway instead of Lambda concurrency directly.
Why the other options are wrong
- A. A concurrency value of 1 cannot handle thousands of daily requests or the 10x peak demand periods, and deleting DAX removes an already-deployed optimization.
- B. Reserved concurrency with value 0 does nothing; it prevents Lambda from scaling and doesn't reduce cold starts.
- D. Scaling API Gateway reserved concurrency does not address Lambda cold starts, which is the root cause of latency.
Question 3
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?
Show answer and explanation
Correct answer: 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.
CodeDeploy provides built-in environment variables during deployment, including DEPLOYMENT_GROUP_NAME, which identifies the current deployment group without requiring custom tagging, EC2 API calls, or custom runbooks. This variable can be accessed in any lifecycle hook script to dynamically configure settings based on the deployment group. Using it in the BeforeInstall hook allows configuration before the application is installed, which is appropriate for setting log level configurations. The approach requires a single script that uses this variable, avoiding the need for different script versions or applications for each environment. Option A requires EC2 tagging and API calls, adding management overhead. Option C requires creating custom environment variables for each deployment group. Option D references a non-standard environment variable DEPLOYMENT_GROUP_ID and uses the Install hook instead of BeforeInstall.
Why the other options are wrong
- A. Querying EC2 metadata and API to determine deployment group adds unnecessary complexity compared to using built-in CodeDeploy environment variables.
- C. Custom environment variables must be manually created for each deployment group, adding management overhead that the built-in DEPLOYMENT_GROUP_NAME variable eliminates.
- D. DEPLOYMENT_GROUP_ID is not a standard CodeDeploy environment variable; DEPLOYMENT_GROUP_NAME is the correct variable to use.
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What’s in the AWS DOP-C02 pack
- 480 questions across all six DOP-C02 domains
- Answers and explanations for every question, including the wrong options
- A questions-only PDF for timed practice runs
- Delivered by email shortly after checkout
- Free monthly updates for as long as the exam is live
- Pass or your money back
A failed DOP-C02 attempt costs another US$300. This pack is US$39, paid once, and refunded if you fail.
What makes the DOP-C02 hard
DOP-C02 is a professional-level exam, and the question length alone wears candidates down. Each scenario is a paragraph of architecture, multi-account, multi-region, pipelines feeding pipelines, and every option is technically valid; the job is to spot the one that best satisfies the requirement buried in the middle.
Multi-account CI/CD with CodePipeline, CloudFormation StackSets and drift, blue/green and canary deployments, EventBridge-driven automation, incident response with Systems Manager, and monitoring and DR design are the decisions a senior DevOps engineer makes every day. Candidates who passed the associate-level exams but have not trained on professional-length scenarios consistently run out of time across 75 dense questions in 180 minutes. This pack has 480 practice questions across all six domains.
About the exam
DOP-C02 certifies DevOps engineers who automate, operate and secure AWS environments at scale, covering CI/CD, infrastructure as code, resilience and incident response with CodePipeline, CloudFormation, Systems Manager and EventBridge. No formal prerequisites, though AWS recommends two or more years operating AWS environments. Valid for three years.
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Yes. The pack is updated every month for as long as the exam is live, and updates are free for everyone who has bought it.
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We refund the pack, or swap it for any other pack if you would rather keep studying. Sit the exam 7 to 30 days after buying, then send your official score report within 7 days of the exam date, as set out in the refund policy.
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99% of customers who prepare with a CertStash pack pass. If you do not, we refund the pack, or swap it for another pack.
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