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DOP-C02 Exam Dumps : AWS Certified DevOps Engineer - Professional

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AWS Certified DevOps Engineer - Professional Questions and Answers

Question 1

A company is implementing an Amazon Elastic Container Service (Amazon ECS) cluster to run its workload. The company architecture will run multiple ECS services on the cluster. The architecture includes an Application Load Balancer on the front end and uses multiple target groups to route traffic.

A DevOps engineer must collect application and access logs. The DevOps engineer then needs to send the logs to an Amazon S3 bucket for near-real-time analysis.

Which combination of steps must the DevOps engineer take to meet these requirements? (Choose three.)

Options:

A.

Download the Amazon CloudWatch Logs container instance from AWS. Configure this instance as a task. Update the application service definitions to include the logging task.

B.

Install the Amazon CloudWatch Logs agent on the ECS instances. Change the logging driver in the ECS task definition to awslogs.

C.

Use Amazon EventBridge to schedule an AWS Lambda function that will run every 60 seconds and will run the Amazon CloudWatch Logs create-export-task command. Then point the output to the logging S3 bucket.

D.

Activate access logging on the ALB. Then point the ALB directly to the logging S3 bucket.

E.

Activate access logging on the target groups that the ECS services use. Then send the logs directly to the logging S3 bucket.

F.

Create an Amazon Kinesis Data Firehose delivery stream that has a destination of the logging S3 bucket. Then create an Amazon CloudWatch Logs subscription filter for Kinesis Data Firehose.

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Question 2

A video-sharing company stores its videos in Amazon S3. The company has observed a sudden increase in video access requests, but the company does not know which videos are most popular. The company needs to identify the general access pattern for the video files. This pattern includes the number of users who access a certain file on a given day, as well as the numb A DevOps engineer manages a large commercial website that runs on Amazon EC2 The website uses Amazon Kinesis Data Streams to collect and process web togs The DevOps engineer manages the Kinesis consumer application, which also runs on Amazon EC2

Sudden increases of data cause the Kinesis consumer application to (all behind and the Kinesis data streams drop records before the records can be processed The DevOps engineer must implement a solution to improve stream handling

Which solution meets these requirements with the MOST operational efficiency ' '

er of pull requests for certain files.

How can the company meet these requirements with the LEAST amount of effort?

Options:

A.

Activate S3 server access logging. Import the access logs into an Amazon Aurora database. Use an Aurora SQL query to analyze the access patterns.

B.

Activate S3 server access logging. Use Amazon Athena to create an external table with the log files. Use Athena to create a SQL query to analyze the access patterns.

C.

Invoke an AWS Lambda function for every S3 object access event. Configure the Lambda function to write the file access information, such as user. S3 bucket, and file key, to an Amazon Aurora database. Use an Aurora SQL query to analyze the access patterns.

D.

Record an Amazon CloudWatch Logs log message for every S3 object access event. Configure a CloudWatch Logs log stream to write the file access information, such as user, S3 bucket, and file key, to an Amazon Kinesis Data Analytics for SQL application. Perform a sliding window analysis.

Question 3

A DevOps engineer manages a Java-based application that runs in an Amazon Elastic Container Service (Amazon ECS) cluster on AWS Fargate. Auto scaling has not been configured for the application. The DevOps engineer has determined that the Java Virtual Machine (JVM) thread count is a good indicator of when to scale the application. The application serves customer traffic on port 8080 and makes JVM metrics available on port 9404. Application use has recently increased. The DevOps engineer needs to configure auto scaling for the application. Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.

Deploy the Amazon CloudWatch agent as a container sidecar. Configure the CloudWatch agent to retrieve JVM metrics from port 9404. Create CloudWatch alarms on the JVM thread count metric to scale the application. Add a step scaling policy in Fargate to scale up and scale down based on the CloudWatch alarms.

B.

Deploy the Amazon CloudWatch agent as a container sidecar. Configure a metric filter for the JVM thread count metric on the CloudWatch log group for the CloudWatch agent. Add a target tracking policy in Fargate. Select the metric from the metric filter as a scale target.

C.

Create an Amazon Managed Service for Prometheus workspace. Deploy AWS Distro for OpenTelemetry as a container sidecar to publish the JVM metrics from port 9404 to the Prometheus workspace. Configure rules for the workspace to use the JVM thread count metric to scale the application. Add a step scaling policy in Fargate. Select the Prometheus rules to scale up and scaling down.

D.

Create an Amazon Managed Service for Prometheus workspace. Deploy AWS Distro for OpenTelemetry as a container sidecar to retrieve JVM metrics from port 9404 to publish the JVM metrics from port 9404 to the Prometheus workspace. Add a target tracking policy in Fargate. Select the Prometheus metric as a scale target.