Online Access Free Amazon.AWS-DevOps-Engineer-Professional.v2022-01-20.q245 Practice Test (Page 30)

AWS-DevOps-Engineer-Professional Exam Question 141

You are designing a service that aggregates clickstream data in batch and delivers reports to subscribers via email only once per week. Data is extremely spikey, geographically distributed, high-scale, and unpredictable.
How should you design this system?

A.Use a large RedShift cluster to perform the analysis, and a fleet of Lambdas to perform record inserts into the RedShift tables. Lambda will scale rapidly enough for the traffic spikes.

B.Use a CloudFront distribution with access log delivery to S3. Clicks should be recorded as querystring GETs to the distribution. Reports are built and sent by periodically running EMR jobs over the access logs in S3.

C.Use API Gateway invoking Lambdas which PutRecords into Kinesis, and EMR running Spark performing GetRecords on Kinesis to scale with spikes. Spark on EMR outputs the analysis to S3, which are sent out via email.

D.Use AWS Elasticsearch service and EC2 Auto Scaling groups. The Autoscaling groups scale based on click throughput and stream into the Elasticsearch domain, which is also scalable. Use Kibana to generate reports periodically.

AWS-DevOps-Engineer-Professional Exam Question 142

You have an application consisting of a stateless web server tier running on Amazon EC2 instances behind load balancer, and are using Amazon RDS with read replicas. Which of the following methods should you use to implement a self-healing and cost-effective architecture? (Choose two.)

A.Use an Amazon RDS Multi-AZ deployment.

B.Set up an Auto Scaling group for the web server tier along with an Auto Scaling policy that uses the Amazon EC2 CPU utilization CloudWatch metric to scale the instances.

C.Use a larger Amazon EC2 instance type for the web server tier and a larger DB instance type for the data storage layer to ensure that they don't become unhealthy.

D.Set up an Auto Scaling group for the database tier along with an Auto Scaling policy that uses the Amazon RDS read replica lag CloudWatch metric to scale out the Amazon RDS read replicas.

E.Set up an Auto Scaling group for the web server tier along with an Auto Scaling policy that uses the Amazon RDS DB CPU utilization CloudWatch metric to scale the instances.

F.Set up a third-party monitoring solution on a cluster of Amazon EC2 instances in order to emit custom CloudWatch metrics to trigger the termination of unhealthy Amazon EC2 instances.

G.Set up scripts on each Amazon EC2 instance to frequently send ICMP pings to the load balancer in order to determine which instance is unhealthy and replace it.

AWS-DevOps-Engineer-Professional Exam Question 143

Your application has an Auto Scaling group of m3.large instances running an application that receives messages born an Amazon SQS queue.
After a while, the number of instances reaches the maximum set for the group and the number of messages on the queue continues to increase.
You have discovered that a third- party library used by the application has a bug that causes a memory leak.
What cost-effective steps can you take to continue message processing while the library developer fixes the bug?

A.Use application monitoring on the instance to restart the application when memory usage reaches a defined level.

B.Use Amazon EC2 instance memory usage CloudWatch metrics to raise alerts when they reach a defined level and send a message to Auto Scaling to fail the instance health check.

C.Create a new Auto Scaling launch configuration to use the r3.large instance type.
Update the Auto Scaling group with the new launch configuration.

D.Enable Elastic Load Balancing health checks for the Auto Scaling group.
When Elastic Load Balancing has detected a failure, Auto Scaling will terminate the failing application's instance and launch a new one.

AWS-DevOps-Engineer-Professional Exam Question 144

A DevOps Engineer is developing a deployment strategy that will allow for data-driven decisions before a feature is fully approved for general availability. The current deployment process uses AWS CloudFormation and blue/green-style deployments. The development team has decided that customers should be randomly assigned to groups, rather than using a set percentage, and redirects should be avoided.
What process should be followed to implement the new deployment strategy?

A.Configure Amazon CloudFront with an AWS Lambda@Edge function to set a cookie when CloudFront receives a request. Assign the user to a version A or B, then return the corresponding version to the viewer.

B.Configure Amazon Route 53 with an AWS Lambda function to set a cookie when Amazon CloudFront receives a request. Assign the user to version A or B, then return the corresponding version to the viewer.

C.Configure Amazon CloudFront with an AWS Lambda@Edge function to set a cookie when CloudFront receives a request. Assign the user to a version A or B, and configure the web server to redirect to version A or B.

D.Configure Amazon Route 53 weighted records for the blue and green stacks, with 50% of traffic configured to route to each stack.

AWS-DevOps-Engineer-Professional Exam Question 145

If designing a single playbook to run across multiple Linux distributions that have distribution specific commands, what would be the best method to allow a successful run?

A.Enable fact gathering and use the `when' conditional to match the distribution to the task.

B.This isn't possible, a separate playbook for each target Linux distribution is required.

C.Use `ignore_errors: true' in the tasks.

D.Use the `shell' module to write your own checks for each command that is ran.

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AWS-DevOps-Engineer-Professional Exam Question 141

AWS-DevOps-Engineer-Professional Exam Question 142

AWS-DevOps-Engineer-Professional Exam Question 143

AWS-DevOps-Engineer-Professional Exam Question 144

AWS-DevOps-Engineer-Professional Exam Question 145

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