DOP-C02 Amazon Web Services AWS Certified DevOps Engineer - Professional Free Practice Exam Questions (2026 Updated)

AWS recommends centralizing CodeDeploy agent logs in Amazon CloudWatch Logs for efficient analysis. By configuring the CloudWatch agent or using the CodeDeploy console integration, you can stream logs in near-real time and query them with CloudWatch Logs Insights. This enables filtering by deployment lifecycle events, saving time versus manually connecting to instances. Refer to AWS documentation section “Monitoring and troubleshooting deployments with CodeDeploy and CloudWatch.”

The latency in processing and ingesting logs can be caused by several factors, such as the throughput of the Kinesis data stream, the concurrency of the Lambda function, and the configuration of the event source mapping. To reduce the latency, the following steps can be taken:

Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer. This will allow the Lambda function to receive records from the data stream with dedicated throughput of up to 2 MB per second per shard, independent of other consumers1. This will reduce the contention and delay in accessing the data stream.

Increase the ParallelizationFactor setting in the Lambda event source mapping. This will allow the Lambda service to invoke more instances of the function concurrently to process the records from the data stream2. This will increase the processing capacity and reduce the backlog of records in the data stream.

Configure reserved concurrency for the Lambda function that processes the logs. This will ensure that the function has enough concurrency available to handle the increased load from the data stream3. This will prevent the function from being throttled by the account-level concurrency limit.

The other options are not effective or may have negative impacts on the latency. Option D is not suitable because increasing the batch size in the Kinesis data stream will increase the amount of data that the Lambda function has to process in each invocation, which may increase the execution time and latency4. Option E is not advisable because turning off the ReportBatchItemFailures setting in the Lambda event source mapping will prevent the Lambda service from retrying the failed records, which may result in data loss. Option F is not necessary because increasing the number of shards in the Kinesis data stream will increase the throughput of the data stream, but it will not affect the processing speed of the Lambda function, which is the bottleneck in this scenario.

1: Using AWS Lambda with Amazon Kinesis Data Streams - AWS Lambda

2: AWS Lambda event source mappings - AWS Lambda

3: Managing concurrency for a Lambda function - AWS Lambda

4: AWS Lambda function scaling - AWS Lambda

AWS Lambda event source mappings - AWS Lambda

Scaling Amazon Kinesis Data Streams with AWS CloudFormation - Amazon Kinesis Data Streams

To invoke many Lambda functions in parallel and allow each function to have independent retry logic and concurrency management, using SQS queues for each Lambda function is recommended.

The BeginResponse Lambda publishes a message to an SNS topic, which fans out to multiple SQS 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.

To meet the requirements of ensuring that flow logs remain configured for all existing and new VPCs in the AWS account, the company should use AWS Config and automatic remediation. AWS Config is a service that enables customers to assess, audit, and evaluate the configurations of their AWS resources. AWS Config continuously monitors and records the configuration changes of the AWS resources and evaluates them against desired configurations. Customers can use AWS Config rules to define the desired configuration state of their AWS resources and trigger actions when a resource configuration violates a rule.

One of the AWS Config rules that customers can use is vpc-flow-logs-enabled, which checks whether VPC flow logs are enabled for all VPCs in an AWS account. Customers can also configure automatic remediation for this rule, which means that AWS Config will automatically enable VPC flow logs for any VPCs that do not have them enabled. Customers can specify the destination (CloudWatch Logs or S3) and the traffic type (all, accept, or reject) for the flow logs as remediation parameters. By using AWS Config and automatic remediation, the company can ensure that flow logs remain configured for all existing and new VPCs in its AWS account, regardless of who creates them or how they are created.

The other options are not correct because they do not meet the requirements or follow best practices. Adding the resource to the CloudFormation stack that creates the VPCs is not a sufficient solution because it will only work for VPCs that are created by using the CloudFormation stack. It will not work for VPCs that are created by using other methods, such as the console or the API. Creating an organization in AWS Organizations and creating an SCP to prevent users from modifying VPC flow logs is not a good solution because it will not ensure that flow logs are enabled for all VPCs in the first place. It will only prevent users from disabling or changing flow logs after they are enabled. Creating an IAM policy to deny the use of API calls for VPC flow logs and attaching it to all IAM users is not a valid solution because it will prevent users from enabling or disabling flow logs at all. It will also not work for VPCs that are created by using other methods, such as the console or CloudFormation.

1: AWS::EC2::FlowLog - AWS CloudFormation

2: Amazon VPC Flow Logs extends CloudFormation Support to custom format subscriptions, 1-minute aggregation intervals and tagging

3: Logging IP traffic using VPC Flow Logs - Amazon Virtual Private Cloud

About AWS Config - AWS Config

vpc-flow-logs-enabled - AWS Config

Remediate Noncompliant Resources with AWS Config Rules - AWS Config

A zonal shift in Amazon Application Recovery Controller is the correct mechanism for quickly moving traffic away from an impaired Availability Zone while keeping the application active in healthy zones. AWS states that zonal shift causes the load balancer to stop sending new traffic to the affected Availability Zone. For ALB targets, the shifted-away targets receive an AdministrativeOverride state, and the ALB stops routing new traffic to them while existing connections close normally. Disabling cross-zone load balancing at the target group level supports the static-stability goal because traffic and capacity remain zonally scoped. Option B is manual and slower. Option C leaves cross-zone inheritance in place, which weakens zonal isolation. Option D changes subnet configuration and is operationally riskier than ARC zonal shift.

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CloudWatch metric filters can parse logs directly to create metrics without additional infrastructure.

Metric filters combined with CloudWatch dashboards provide the simplest and most operationally efficient solution.

Options A, B, and D add complexity with additional services (OpenSearch, Lambda, Kinesis).

CodePipeline V2 with QUEUED mode ensures that new executions wait for the previous execution to finish, preventing overlapping runs.

Adding a trigger filter to listen for Git tags triggers the pipeline only on new tag pushes.

An EventBridge rule can detect new image pushes to ECR and start the deployment pipeline, integrating the build and deploy pipelines effectively.

SUPERSEDED mode cancels the previous run when a new one starts, which is not desired here.

Using branches instead of tags would trigger on all commits, not just releases.

To implement governance controls that restrict AWS service usage to within the United States and ensure alerts for any activity outside the governance policy, the following actions will meet the requirements:

A. Create an AWS Organizations SCP that denies access to all non-global services in non-US Regions. Attach the policy to the root of the organization. This action will effectively prevent users and roles in all accounts within the organization from accessing services in non-US Regions12.

B. Configure AWS CloudTrail to send logs to Amazon CloudWatch Logs and enable it for all Regions. Use a CloudWatch Logs metric filter to send an alert on any service activity in non-US Regions. This action will allow monitoring of all AWS Regions and will trigger alerts if any activity is detected in non-US Regions, ensuring that the governance team is notified as soon as possible3.

AWS Documentation on Service Control Policies (SCPs) and how they can be used to manage permissions and restrict access based on Regions12.

AWS Documentation on monitoring CloudTrail log files with Amazon CloudWatch Logs to set up alerts for specific activities3.

The build must run in the shared services account, while deployments must reach separate test and production accounts. Cross-account CodePipeline actions require roles in the target accounts and access to the pipeline artifact bucket. Therefore, the design needs a CodePipeline execution role in shared services, target-account cross-account roles, and a CodeBuild service role. CodePipeline artifacts belong in Amazon S3, while Lambda container images belong in Amazon ECR; CodeArtifact is for software packages, not pipeline artifacts or container image storage. The pipeline stages also need to reflect the actual delivery model: source, build container image, deploy to test, then deploy to production. AWS guidance shows CodeBuild creating Docker images and publishing them to ECR, which Lambda then uses as the container image source.