Amazon AWS Certified DevOps Engineer - Professional DOP-C02 Exam Questions

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Question 1

An ecommerce company is receiving reports that its order history page is experiencing delays in reflecting the processing status of orders. The order processing system consists of an AWS Lambda function that uses reserved concurrency. The Lambda function processes order messages from an Amazon Simple Queue Service (Amazon SQS) queue and inserts processed orders into an Amazon DynamoDB table. The DynamoDB table has auto scaling enabled for read and write capacity.

Which actions should a DevOps engineer take to resolve this delay? (Choose two.)

ACheck the ApproximateAgeOfOldestMessage metric for the SQS queue. Increase the Lambda function concurrency limit.

BCheck the ApproximateAgeOfOldestMessage metnc for the SQS queue Configure a redrive policy on the SQS queue.

CCheck the NumberOfMessagesSent metric for the SQS queue. Increase the SQS queue visibility timeout.

DCheck the WriteThrottleEvents metric for the DynamoDB table. Increase the maximum write capacity units (WCUs) for the table's scaling policy.

ECheck the Throttles metric for the Lambda function. Increase the Lambda function timeout.

Answer : A, D

A: If the ApproximateAgeOfOldestMessages indicate that orders are remaining in the SQS queue for longer than expected, the reserved concurrency limit may be set too small to keep up with the number of orders entering the queue and is being throttled. D: The DynamoDB table is using Auto Scaling. With Auto Scaling, you create a scaling policy that specifies whether you want to scale read capacity or write capacity (or both), and the minimum and maximum provisioned capacity unit settings for the table. The ThottledWriteRequests metric will indicate if there is a throttling issue on the DynamoDB table, which can be resolved by increasing the maximum write capacity units for the table's Auto Scaling policy. https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/AutoScaling.html

Question 2

A large enterprise is deploying a web application on AWS. The application runs on Amazon EC2 instances behind an Application Load Balancer. The instances run in an Auto Scaling group across multiple Availability Zones. The application stores data in an Amazon RDS for Oracle DB instance and Amazon DynamoDB. There are separate environments tor development testing and production.

What is the MOST secure and flexible way to obtain password credentials during deployment?

ARetrieve an access key from an AWS Systems Manager securestring parameter to access AWS services. Retrieve the database credentials from a Systems Manager SecureString parameter.

BLaunch the EC2 instances with an EC2 1AM role to access AWS services Retrieve the database credentials from AWS Secrets Manager.

CRetrieve an access key from an AWS Systems Manager plaintext parameter to access AWS services. Retrieve the database credentials from a Systems Manager SecureString parameter.

DLaunch the EC2 instances with an EC2 1AM role to access AWS services Store the database passwords in an encrypted config file with the application artifacts.

Answer : B

AWS Secrets Manager is a secrets management service that helps you protect access to your applications, services, and IT resources. This service enables you to easily rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle. Using Secrets Manager, you can secure and manage secrets used to access resources in the AWS Cloud, on third-party services, and on-premises. SSM parameter store and AWS Secret manager are both a secure option. However, Secrets manager is more flexible and has more options like password generation. Reference: https://www.1strategy.com/blog/2019/02/28/aws-parameter-store-vs-aws-secrets-manager/

Question 3

A DevOps engineer needs to apply a core set of security controls to an existing set of AWS accounts. The accounts are in an organization in AWS Organizations. Individual teams will administer individual accounts by using the AdministratorAccess AWS managed policy. For all accounts. AWS CloudTrail and AWS Config must be turned on in all available AWS Regions. Individual account administrators must not be able to edit or delete any of the baseline resources. However, individual account administrators must be able to edit or delete their own CloudTrail trails and AWS Config rules.

Which solution will meet these requirements in the MOST operationally efficient way?

ACreate an AWS CloudFormation template that defines the standard account resources. Deploy the template to all accounts from the organization's management account by using CloudFormation StackSets. Set the stack policy to deny Update:Delete actions.

BEnable AWS Control Tower. Enroll the existing accounts in AWS Control Tower. Grant the individual account administrators access to CloudTrail and AWS Config.

CDesignate an AWS Config management account. Create AWS Config recorders in all accounts by using AWS CloudFormation StackSets. Deploy AWS Config rules to the organization by using the AWS Config management account. Create a CloudTrail organization trail in the organization's management account. Deny modification or deletion of the AWS Config recorders by using an SCP.

DCreate an AWS CloudFormation template that defines the standard account resources. Deploy the template to all accounts from the organization's management account by using Cloud Formation StackSets Create an SCP that prevents updates or deletions to CloudTrail resources or AWS Config resources unless the principal is an administrator of the organization's management account.

Answer : D

Question 4

A company has a legacy application A DevOps engineer needs to automate the process of building the deployable artifact for the legacy application. The solution must store the deployable artifact in an existing Amazon S3 bucket for future deployments to reference

Which solution will meet these requirements in the MOST operationally efficient way?

ACreate a custom Docker image that contains all the dependencies tor the legacy application Store the custom Docker image in a new Amazon Elastic Container Registry (Amazon ECR) repository Configure a new AWS CodeBuild project to use the custom Docker image to build the deployable artifact and to save the artifact to the S3 bucket.

BLaunch a new Amazon EC2 instance Install all the dependencies (or the legacy application on the EC2 instance Use the EC2 instance to build the deployable artifact and to save the artifact to the S3 bucket.

CCreate a custom EC2 Image Builder image Install all the dependencies for the legacy application on the image Launch a new Amazon EC2 instance from the image Use the new EC2 instance to build the deployable artifact and to save the artifact to the S3 bucket.

DCreate an Amazon Elastic Kubernetes Service (Amazon EKS) cluster with an AWS Fargate profile that runs in multiple Availability Zones Create a custom Docker image that contains all the dependencies for the legacy application Store the custom Docker image in a new Amazon Elastic Container Registry (Amazon ECR) repository Use the custom Docker image inside the EKS cluster to build the deployable artifact and to save the artifact to the S3 bucket.

Answer : A

This approach is the most operationally efficient because it leverages the benefits of containerization, such as isolation and reproducibility, as well as AWS managed services. AWS CodeBuild is a fully managed build service that can compile your source code, run tests, and produce deployable software packages. By using a custom Docker image that includes all dependencies, you can ensure that the environment in which your code is built is consistent. Using Amazon ECR to store Docker images lets you easily deploy the images to any environment. Also, you can directly upload the build artifacts to Amazon S3 from AWS CodeBuild, which is beneficial for version control and archival purposes.

Question 5

A DevOps engineer is building a continuous deployment pipeline for a serverless application that uses AWS Lambda functions. The company wants to reduce the customer impact of an unsuccessful deployment. The company also wants to monitor for issues.

Which deploy stage configuration will meet these requirements?

AUse an AWS Serverless Application Model (AWS SAM) template to define the serverless application. Use AWS CodeDeploy to deploy the Lambda functions with the Canary10Percent15Minutes Deployment Preference Type. Use Amazon CloudWatch alarms to monitor the health of the functions.

BUse AWS CloudFormation to publish a new stack update, and include Amazon CloudWatch alarms on all resources. Set up an AWS CodePipeline approval action for a developer to verify and approve the AWS CloudFormation change set.

CUse AWS CloudFormation to publish a new version on every stack update, and include Amazon CloudWatch alarms on all resources. Use the RoutingConfig property of the AWS::Lambda::Alias resource to update the traffic routing during the stack update.

DUse AWS CodeBuild to add sample event payloads for testing to the Lambda functions. Publish a new version of the functions, and include Amazon CloudWatch alarms. Update the production alias to point to the new version. Configure rollbacks to occur when an alarm is in the ALARM state.

Answer : D

Use routing configuration on an alias to send a portion of traffic to a second function version. For example, you can reduce the risk of deploying a new version by configuring the alias to send most of the traffic to the existing version, and only a small percentage of traffic to the new version. https://docs.aws.amazon.com/lambda/latest/dg/configuration-aliases.html

The following are the steps involved in the deploy stage configuration that will meet the requirements:

Use AWS CodeBuild to add sample event payloads for testing to the Lambda functions.

Publish a new version of the functions, and include Amazon CloudWatch alarms.

Update the production alias to point to the new version.

Configure rollbacks to occur when an alarm is in the ALARM state.

This configuration will help to reduce the customer impact of an unsuccessful deployment by deploying the new version of the functions to a staging environment first. This will allow the DevOps engineer to test the new version of the functions before deploying it to production.

The configuration will also help to monitor for issues by including Amazon CloudWatch alarms. These alarms will alert the DevOps engineer if there are any problems with the new version of the functions.

Question 6

A company has an application that runs on AWS Lambda and sends logs to Amazon CloudWatch Logs. An Amazon Kinesis data stream is subscribed to the log groups in CloudWatch Logs. A single consumer Lambda function processes the logs from the data stream and stores the logs in an Amazon S3 bucket.

The company's DevOps team has noticed high latency during the processing and ingestion of some logs.

Which combination of steps will reduce the latency? (Select THREE.)

ACreate a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer.

BIncrease the ParallelizationFactor setting in the Lambda event source mapping.

CConfigure reserved concurrency for the Lambda function that processes the logs.

DIncrease the batch size in the Kinesis data stream.

ETurn off the ReportBatchltemFailures setting in the Lambda event source mapping.

FIncrease the number of shards in the Kinesis data stream.

Answer : A, B, C

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

Question 7

An ecommerce company hosts a web application on Amazon EC2 instances that are in an Auto Scaling group. The company deploys the application across multiple Availability Zones.

Application users are reporting intermittent performance issues with the application.

The company enables basic Amazon CloudWatch monitoring for the EC2 instances. The company identifies and implements a fix for the performance issues. After resolving the issues, the company wants to implement a monitoring solution that will quickly alert the company about future performance issues.

Which solution will meet this requirement?

AEnable detailed monitoring for the EC2 instances. Create custom CloudWatch metrics for application-specific performance indicators. Set up CloudWatch alarms based on the custom metrics. Use CloudWatch Logs Insights to analyze application logs for error patterns.

BUse AWS X-Ray to implement distributed tracing. Integrate X-Ray with Amazon CloudWatch RUM. Use Amazon EventBridge to trigger automatic scaling actions based on custom events.

CUse Amazon CloudFront to deliver the application. Use AWS CloudTrail to monitor API calls. Use AWS Trusted Advisor to generate recommendations to optimize performance. Use Amazon GuardDuty to detect potential performance issues.

DEnable VPC Flow Logs. Use Amazon Data Firehose to stream flow logs to Amazon S3. Use Amazon Athena to analyze the logs and to send alerts to the company.

Answer : A

The company needs fast, proactive alerts for future performance issues, beyond basic EC2 metrics. Option A provides a complete, AWS-native monitoring pattern aligned with best practices:

Enable detailed monitoring on EC2 instances to increase metric resolution (from 5-minute to 1-minute intervals), improving the responsiveness of alarms.

Define custom CloudWatch metrics for application-level indicators such as request latency, error rate, queue depth, or throughput. These metrics can be published from the application or sidecar agents.

Create CloudWatch alarms on both infrastructure (CPU, network, disk) and custom application metrics with thresholds that reflect performance SLOs. Alarms can notify teams via SNS or incident management tools.

Use CloudWatch Logs Insights to analyze logs for recurring error patterns, slow requests, or exceptions when alarms fire.

Option B focuses on tracing and frontend RUM; while useful, it is more complex and not necessary just to get quick alerts. Option C uses services (CloudTrail, GuardDuty, Trusted Advisor) that are not focused on real-time performance detection. Option D with VPC Flow Logs is network-level and would not detect general application performance issues.

Thus, Option A offers a direct, efficient way to detect and alert on performance degradations quickly.