DOP-C02 Amazon Web Services AWS Certified DevOps Engineer - Professional Free Practice Exam Questions (2025 Updated)

Comprehensive and Detailed Explanation From Exact Extract:

Amazon Redshift supportsaudit loggingwhich can be configured to log user activity, including queries and user changes. The logs can be delivered to anS3 bucket(Option B), which is the standard location for Redshift audit logs.

While Redshift can send some logs to CloudWatch, the native audit logging is typically routed to S3. Creating a CloudWatch dashboard with a log widget (Option D) allows visualization of user activities from logs ingested into CloudWatch Logs or through custom processing.

Option A is invalid because CloudTrail does not capture Redshift query logs directly. Option C incorrectly assumes Redshift audit logs can be delivered directly to CloudWatch. Option E adds complexity by requiring Lambda and Athena unnecessarily when direct visualization in CloudWatch is simpler.

https://aws.amazon.com/blogs/mt/monitor-changes-and-auto-enable-logging-in-aws-cloudtrail/

To meet the requirements, the DevOps team needs to configure a monitoring solution for the VPC flow logs that can detect anomalies in network traffic over time and initiate a response to the anomaly. The DevOps team can use Amazon Kinesis Data Streams to ingest and process streaming data from CloudWatch Logs. The DevOps team can subscribe the log group to a Kinesis data stream, which will deliver log events from CloudWatch Logs to Kinesis Data Streams in near real-time. The DevOps team can then create an AWS Lambda function to detect log anomalies using machine learning or statistical methods. The Lambda function can be set as a processor for the data stream, which means that it will process each record from the stream before sending it to downstream applications or destinations. The Lambda function can also write to the default Amazon EventBridge event bus if it detects an anomaly, which will allow other AWS services or custom applications to respond to the anomaly event.

B. Update the CodeBuild project role with the necessary permissions and then remove the AWS credentials from the environment variable. C. Store the DB_PASSWORD as a SecureString value in AWS Systems Manager Parameter Store and then remove the DB_PASSWORD from the environment variables. E. Use AWS Systems Manager run command versus scp and ssh commands directly to the instance.

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/

When AWS CodeDeploy performs an in-place deployment, it updates the instances with the new application revision one at a time, as specified by the deployment configuration codeDeployDefault.oneAtATime. If a lifecycle event hook, such as AfterInstall, fails during the deployment, CodeDeploy will attempt to roll back to the previous version on the affected instances. This is likely what happened with the two instances that still have the previous application revision deployed. The failure of the AfterInstall lifecycle event hook triggered the rollback mechanism, resulting in those instances reverting to the previous application revision.

AWS CodeDeploy documentation on redeployment and rollback procedures1.

Stack Overflow discussions on re-deploying older revisions with AWS CodeDeploy2.

AWS CLI reference guide for deploying a revision2.

Amazon ECR enhanced scanninguses Amazon Inspector for vulnerability detection.

EventBridge can capture Inspector scan findings.

Lambda can process scan findings and reject manual approval if critical vulnerabilities exist.

Options A and C use incorrect or less integrated services (basic scanning or Detective).

Option B adds unnecessary complexity with SBOM and Athena.

A comprehensive and detailed explanation is:

Option A is correct because using batch writes to write multiple log events in a single write operation is a recommended practice for optimizing the performance and cost of data ingestion in Timestream. Batch writes can reduce the number of network round trips and API calls, and can also take advantage of parallel processing by Timestream. Batch writes can also improve the compression ratio of data in the memory store and the magnetic store, which can reduce the storage costs and improve the query performance1.

Option B is incorrect because writing each log event as a single write operation is not a recommended practice for optimizing the performance and cost of data ingestion in Timestream. Writing each log event as a single write operation would increase the number of network round trips and API calls, and would also reduce the compression ratio of data in the memory store and the magnetic store. This would increase the storage costs and degrade the query performance1.

Option C is incorrect because treating each log as a single-measure record is not a recommended practice for optimizing the query performance in Timestream. Treating each log as a single-measure record would result in creating multiple records for each timestamp, which would increase the storage size and the query latency. Moreover, treating each log as a single-measure record would require using joins to query multiple measures for the same timestamp, which would add complexity and overhead to the query processing2.

Option D is correct because treating each log as a multi-measure record is a recommended practice for optimizing the query performance in Timestream. Treating each log as a multi-measure record would result in creating a single record for each timestamp, which would reduce the storage size and the query latency. Moreover, treating each log as a multi-measure record would allow querying multiple measures for the same timestamp without using joins, which would simplify and speed up the query processing2.

Option E is incorrect because configuring the memory store retention period to be longer than the magnetic store retention period is not a valid option in Timestream. The memory store retention period must always be shorter than or equal to the magnetic store retention period. This ensures that data is moved from the memory store to the magnetic store before it expires out of the memory store3.

Option F is correct because configuring the memory store retention period to be shorter than the magnetic store retention period is a valid option in Timestream. The memory store retention period determines how long data is kept in the memory store, which is optimized for fast point-in-time queries. The magnetic store retention period determines how long data is kept in the magnetic store, which is optimized for fast analytical queries. By configuring these retention periods appropriately, you can balance your storage costs and query performance according to your application needs3.

https://aws.amazon.com/premiumsupport/knowledge-center/monitor-security-group-changes-ec2/

Deployment of config.txt file based on the appspec.yml:

The appspec.yml file specifies that config/config.txt should be copied to /usr/local/src/config.txt.

The source: / directive in the appspec.yml indicates that the entire directory structure starting from the root of the application source should be copied to the specified destination, which is /var/www/html.

Result of the Deployment:

The config.txt file will be specifically deployed to /usr/local/src/config.txt as per the explicit file mapping.

The entire directory structure including application/web will be copied to /var/www/html, but this does not include config/config.txt since it has a specific destination defined.

Thus, the config.txt file will be deployed only to /usr/local/src/config.txt.

Therefore, the correct answer is:

C. The config.txt file will be deployed to only /usr/local/src/config.txt.

These are the possible causes for the AccessDenied error because they affect the permissions to access the S3 object from the EC2 instance. An S3 bucket policy is a resource-based policy that defines who can access the bucket and its objects, and what actions they can perform. An IAM role is an identity that can be assumed by an EC2 instance to grant it permissions to access AWS services and resources. If there is an error in the S3 bucket policy or the IAM role configuration, such as a missing or incorrect statement, condition, or principal, then the EC2 instance may not have the necessary permissions to download the object from the S3 bucket .

https://docs.aws.amazon.com/AmazonS3/latest/userguide/example-bucket-policies.html

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/iam-roles-for-amazon-ec2.html

 To optimize the Macie costs for the account without compromising the account’s functionality, the DevOps engineer needs to exclude S3 buckets that do not contain sensitive data from automated discovery. S3 buckets that contain CloudTrail logs are unlikely to have sensitive data, and Macie charges for scanning and monitoring data in S3 buckets. Therefore, excluding S3 buckets that contain CloudTrail logs from automated discovery can reduce Macie costs. Similarly, configuring discovery jobs to include S3 objects based on the last modified criterion can also reduce Macie costs, as it will only scan and monitor new or updated objects, rather than all objects in the bucket.

The following are the steps that the DevOps engineer should take to reduce the latency of the Lambda function at all times of the day:

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 provisioned concurrency setting ensures that there is always a minimum number of Lambda function instances available to handle requests. The Application Auto Scaling setting will automatically scale the number of Lambda function instances up or down based on the demand for the application.

This solution will ensure that the Lambda function is able to handle the increased load during the middle of the day, while also keeping the cold-start latency low.

The following are the reasons why the other options are not correct:

Option A is incorrect because it will not reduce the cold-start latency of the Lambda function.

Option B is incorrect because it will not scale the number of Lambda function instances up or down based on demand.

Option D is incorrect because it will only configure reserved concurrency on the API Gateway API, which will not affect the Lambda function.

Option A is incorrect because increasing the memory of the Lambda function does not address the root cause of the problem, which is that the Lambda function is not triggered by the S3 event source. Increasing the memory of the Lambda function might improve its performance or reduce its execution time, but it does not affect its invocation. Moreover, increasing the memory of the Lambda function might incur higher costs, as Lambda charges based on the amount of memory allocated to the function.

Option B is correct because creating a resource policy on the Lambda function to grant Amazon S3 the permission to invoke the Lambda function for the S3 bucket is a necessary step to configure an S3 event source. A resource policy is a JSON document that defines who can access a Lambda resource and under what conditions. By granting Amazon S3 permission to invoke the Lambda function, the company ensures that the Lambda function runs when a new object is created or an existing object is modified in the S3 bucket1.

Option C is incorrect because configuring an Amazon Simple Queue Service (Amazon SQS) queue as an On-Failure destination for the Lambda function does not help with triggering the Lambda function. An On-Failure destination is a feature that allows Lambda to send events to another service, such as SQS or Amazon Simple Notification Service (Amazon SNS), when a function invocation fails. However, this feature only applies to asynchronous invocations, and S3 event sources use synchronous invocations. Therefore, configuring an SQS queue as an On-Failure destination would have no effect on the problem.

Option D is incorrect because provisioning space in the /tmp folder of the Lambda function does not address the root cause of the problem, which is that the Lambda function is not triggered by the S3 event source. Provisioning space in the /tmp folder of the Lambda function might help with processing large files from the S3 bucket, as it provides temporary storage for up to 512 MB of data. However, it does not affect the invocation of the Lambda function.

Comprehensive and Detailed Explanation From Exact Extract:

Step A: Using a tool like CloudFormation resource import or IaC generator to scan and create a template from existing resources is efficient to bring current infrastructure under management.

Step C: UsingCodeConnections(AWS's solution to connect Git repositories) with AWS CodePipeline ensures any changes to CloudFormation templates in the Git repo automatically deploy infrastructure changes, enforcing infrastructure as code workflows.

Step E: Creating anIAM role with CloudFormation as the principalensures CloudFormation has permissions to manage resources. Using anSCP to deny all actions except by this roleenforces strict control, preventing manual changes outside the pipeline.Option B uses AWS Config which is more for compliance and auditing, not direct resource import. Option D is invalid because CloudFormation does not natively sync with Git; CodePipeline does. Option F is less secure than denying all except the IAM role.

Comprehensive and Detailed Explanation From Exact Extract:

To invoke many Lambda functionsin paralleland allow each function to haveindependent retry logic and concurrency management, usingSQS queues for each Lambda functionis recommended.

The BeginResponse Lambda publishes a message to anSNS topic, which fans out to multipleSQS queues(one per Lambda).

Each Lambda function polls its own SQS queue, allowing fine-grained control of concurrency and retry behavior.

SNS alone (Option A) invokes Lambda functions but lacks the queue’s buffering and retry durability.

Step Functions (Option D) would invoke Lambdas sequentially, not in parallel, and add complexity.

Option C reverses SNS and SQS in an inefficient manner.

You can run custom recipes manually, but the best approach is usually to have AWS OpsWorks Stacks run them automatically. Every layer has a set of built-in recipes assigned each of five lifecycle events—Setup, Configure, Deploy, Undeploy, and Shutdown. Each time an event occurs for an instance, AWS OpsWorks Stacks runs the associated recipes for each of the instance's layers, which handle the corresponding tasks. For example, when an instance finishes booting, AWS OpsWorks Stacks triggers a Setup event. This event runs the associated layer's Setup recipes, which typically handle tasks such as installing and configuring packages