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Question # 1
A company has deployed an Amazon Elastic Kubernetes Service (Amazon EKS) cluster
with Amazon EC2 node groups. The company's DevOps team uses the Kubernetes
Horizontal Pod Autoscaler and recently installed a supported EKS cluster Autoscaler.
The DevOps team needs to implement a solution to collect metrics and logs of the EKS
cluster to establish a baseline for performance. The DevOps team will create an initial set
of thresholds for specific metrics and will update the thresholds over time as the cluster is
used. The DevOps team must receive an Amazon Simple Notification Service (Amazon
SNS) email notification if the initial set of thresholds is exceeded or if the EKS cluster
Autoscaler is not functioning properly.
The solution must collect cluster, node, and pod metrics. The solution also must capture
logs in Amazon CloudWatch.
Which combination of steps should the DevOps team take to meet these requirements?
(Select THREE.)
A. Deploy the CloudWatch agent and Fluent Bit to the cluster. Ensure that the EKS cluster has appropriate permissions to send metrics and logs to CloudWatch.
B. Deploy AWS Distro for OpenTelemetry to the cluster. Ensure that the EKS cluster has appropriate permissions to send metrics and logs to CloudWatch.
C. Create CloudWatch alarms to monitor the CPU, memory, and node failure metrics of the cluster. Configure the alarms to send an SNS email notification to the DevOps team if thresholds are exceeded.
D. Create a CloudWatch composite alarm to monitor a metric log filter of the CPU, memory, and node metrics of the cluster. Configure the alarm to send an SNS email notification to the DevOps team when anomalies are detected.
E. Create a CloudWatch alarm to monitor the logs of the Autoscaler deployments for errors. Configure the alarm to send an SNS email notification to the DevOps team if thresholds are exceeded.
F. Create a CloudWatch alarm to monitor a metric log filter of the Autoscaler deployments for errors. Configure the alarm to send an SNS email notification to the DevOps team if thresholds are exceeded.
Question # 2
A video-sharing company stores its videos in an Amazon S3 bucket. The company needs
to analyze user access patterns such as the number of users who access a specific video
each month.
Which solution will meet these requirements with the LEAST development effort?
A. Enable Amazon S3 server access logging. Load the access logs into an Amazon Aurora database. Run SQL queries on the Aurora database to analyze the user access patterns.
B. Enable Amazon S3 server access logging. Use Amazon Athena to create an external table that contains the access logs. Run SQL queries on the Athena table to analyze the user access patterns.
C. Invoke an AWS Lambda function for every S3 object access event. Configure the Lambda function to write the file access information, including user ID, S3 bucket ID, and file key, to an Amazon Aurora database. Run SQL queries on the Aurora database to analyze the user access patterns.
D. Record a log message in Amazon CloudWatch Logs for every S3 object access event. Configure a log stream in CloudWatch Logs to write the file access information, including user ID, S3 bucket ID, and file key, to an Amazon Managed Service for Apache Flink application. Perform a sliding window analysis on the user access patterns.
Question # 3
A company has a stateless web application that is deployed on Amazon EC2 instances.
The EC2 instances are in a target group behind an Application Load Balancer (ALB).
Amazon Route 53 manages the application domain. The company updates the application UI and develops a beta version of the application.
The company wants to test the beta version on 10% of its traffic.
Which solution will meet these requirements with the LEAST number of configuration
changes?
A. Deploy the beta version to new EC2 instances in a new target group. Associate the new target group with a new ALB. Update the existing Route 53 record to use a weighted routing policy. Add a new Route 53 record that points to the new ALB with the same routing policy. Assign a weight of 90 to the existing record. Assign a weight of 10 to the new record.
B. Deploy the beta version to new EC2 instances in a new target group. Associate the new target group with the same ALB listener rule. Assign a weight of 90 to the existing target group. Assign a weight of 10 to the new target group.
C. Refactor the application to implement a feature flag for the beta version by using AWS AppConfig. Use the feature flag to enable the beta version for 10% of the EC2 instances.
D. Containerize and deploy the application on Amazon Elastic Container Service (Amazon ECS). Use AWS CodeDeploy to deploy the beta version by using the CodeDeployDefault.ECSCanary10Percent15Minutes deployment configuration.
Question # 4
A company has an application that runs on Amazon EC2 instances in an Auto Scaling
group. The application processes a high volume of messages from an Amazon Simple
Queue Service (Amazon SQS) queue.
A DevOps engineer noticed that the application took several hours to process a group of
messages from the SQS queue. The average CPU utilization of the Auto Scaling group did
not cross the threshold of a target tracking scaling policy when processing the messages.
The application that processes the SQS queue publishes logs to Amazon CloudWatch
Logs.
The DevOps engineer needs to ensure that the queue is processed quickly.
Which solution meets these requirements with the LEAST operational overhead?
A. Create an AWS Lambda function. Configure the Lambda function to publish a custom metric by using the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupIn-ServiceInstances Auto Scaling group attribute to publish the queue messages for each instance. Schedule an Amazon EventBridge rule to run the Lambda function every hour. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.
B. Create an AWS Lambda function. Configure the Lambda function to publish a custom metric by using the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupIn-ServiceInstances Auto Scaling group attribute to publish the queue messages for each instance. Create a CloudWatch subscription filter for the application logs with the Lambda function as the target. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.
C. Create a target tracking scaling policy for the Auto Scaling group. In the target tracking policy, use the ApproximateNumberOfMessagesVisible SQS queue attribute and the GroupIn-ServiceInstances Auto Scaling group attribute to calculate how many messages are in the queue for each number of instances by using metric math. Use the calculated attribute to scale in and out.
D. Create an AWS Lambda function that logs the ApproximateNumberOfMessagesVisible attribute of the SQS queue to a CloudWatch Logs log group. Schedule an Amazon EventBridge rule to run the Lambda function every 5 minutes. Create a metric filter to count the number of log events from a CloudWatch logs group. Create a target tracking scaling policy for the Auto Scaling group that uses the custom metric to scale in and out.
Question # 5
A company is developing a mobile app that requires extensive automated testing across
multiple device types. The company is using AWS CodePipeline for its CI/CD pipeline. The
company must implement a scalable testing solution that can handle increased test loads
as the app grows. Which solution will meet these requirements with the LEAST
management overhead?
A. Integrate AWS Device Farm with the pipeline to run the tests and scale as needed.
B. Deploy a fleet of Amazon EC2 instances with various mobile device emulators and auto scaling to run the tests. Create a custom AWS Lambda function to invoke EC2 test runs.
C. Implement a containerized testing solution that uses Amazon Elastic Container Service (Amazon ECS) with auto scaling. Configure the pipeline to invoke an AWS Lambda function to start the test runs on the ECS cluster.
D. Use AWS Lambda functions with custom runtime emulators to run the tests. Integrate the Lambda functions with the pipeline.
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