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The AWS Certified DevOps Engineer - Professional certification exam is a valuable credential for professionals who are looking to demonstrate their expertise in AWS services and DevOps practices. AWS Certified DevOps Engineer - Professional certification validates the candidate's ability to design, manage, and maintain AWS-based applications and infrastructures, as well as their proficiency in implementing and managing continuous delivery systems and methodologies. With the increasing demand for DevOps professionals in the industry, obtaining this certification can be a significant boost to one's career prospects.

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To prepare for the AWS Certified DevOps Engineer - Professional exam, candidates should have experience working with AWS services and should have a strong understanding of DevOps principles and practices. Candidates should also be familiar with continuous integration and continuous delivery (CI/CD) tools and should have experience with infrastructure as code (IAC) tools such as CloudFormation and Terraform. Additionally, candidates should be familiar with monitoring and logging tools such as CloudWatch and should have a strong understanding of security and compliance best practices.

Amazon AWS Certified DevOps Engineer - Professional Sample Questions (Q129-Q134):

NEW QUESTION # 129
You are in charge of designing a number of Cloudformation templates for your organization. You need to
ensure that no one can accidentally update the production based resources on the stack during a stack update.
How can this be achieved in the most efficient way?

A. UseMFA to protect the resources
B. Createtags for the resources and then create 1AM policies to protect the resources.
C. Usea Stack based policy to protect the production based resources.
D. UseS3 bucket policies to protect the resources.

Answer: C

Explanation:
Explanation
The AWS Documentation mentions
When you create a stack, all update actions are allowed on all resources. By default, anyone with stack update
permissions can update all of the resources in the stack. During an update, some resources might require an
interruption or be completely replaced, resulting in new physical IDs or completely new storage. You can
prevent stack resources from being unintentionally updated or deleted during a stack update by using a stack
policy. A stack policy is a JSON document that defines
the update action1.-; that car1 be performed on designated resources.
For more information on protecting stack resources, please visit the below url
http://docs.aws.amazon.com/AWSCIoudFormation/latest/UserGuide/protect-stack-resou
rces.html

NEW QUESTION # 130
What is the only layer in a Docker image that is not read-only?

A. the first layer
B. the last layer
C. they are all read-only
D. none are read-only

Answer: B

Explanation:
A Docker image is built up from a series of layers. Each layer represents an instruction in the image's Dockerfile. Each layer except the very last one is read-only.
Reference:
https://docs.docker.com/engine/userguide/storagedriver/imagesandcontainers/#images-and- layers

NEW QUESTION # 131
For AWS Auto Scaling, what is the first transition state an instance enters after leaving steady state when
scaling in due to health check failure or decreased load?

A. Detaching
B. Terminating
C. EnteringStandby
D. Terminating:Wait

Answer: B

Explanation:
When Auto Scaling responds to a scale in event, it terminates one or more instances. These instances
are detached from the Auto Scaling group and enter the Terminating state.
Reference:
http://docs.aws.amazon.com/AutoScaling/latest/DeveloperGuide/AutoScalingGroupLifecycle.html

NEW QUESTION # 132
You have an application running on multiple Amazon EC2 instances within an Auto Scaling group.
You notice that instances are being re-spawned as their health checks are failing in Amazon EC2.
However, before you have a chance to diagnose the issue, the affected instances are being terminated by the Auto Scaling service.
You receive notifications of health checks failing and investigate within 20 minutes.
However, this is not enough time to troubleshoot the issue.
What should you change that will enable you to troubleshoot the instance before it is terminated by the Auto Scaling service, while keeping costs minimal?

A. Configure an Amazon SNS topic and associate it with your Auto Scaling group's CloudWatch alarms.
Configure an Amazon SQS queue as a subscriber of this topic, and then create a fleet of Amazon EC2 workers that poll this queue and instruct the Amazon EC2 Auto Scaling API to remove the instance from the Auto Scaling group when an alarm is triggered.
B. Change the "DeleteOnTermination" flag to false in the Auto Scaling group configuration to ensure that instances are not deleted in the future.
C. Install the Amazon CloudWatch Logs Agent on the instance and configure application and system logs to be sent to the CloudWatch Logs service.
D. Create an Auto Scaling Group lifecycle hook to hold the instance in a terminating:wait state until you have completed any troubleshooting.
When you have completed troubleshooting, wait for the terminating state to expire, or notify to Scaling to complete the lifecycle hook and terminate the Instance.

Answer: D

NEW QUESTION # 133
An application has microservices spread across different AWS accounts and is integrated with an on-premises legacy system for some of its functionality. Because of the segmented architecture and missing logs, every time the application experiences issues, it is taking too long to gather the logs to identify the issues. A DevOps Engineer must fix the log aggregation process and provide a way to centrally analyze the logs.
Which is the MOST efficient and cost-effective solution?

A. Collect system logs and application logs by using the Amazon CloudWatch Logs agent. Use the Amazon S3 API to export on-premises logs, and store the logs in an S3 bucket in a central account.
Build an Amazon EMR cluster to reduce the logs and derive the root cause.
B. Collect system logs and application logs by using the Amazon CloudWatch Logs agent. Use the Amazon S3 API to import on-premises logs. Store all logs in S3 buckets in individual accounts. Use Amazon Macie to write a query to search for the required specific event-related data point.
C. Collect system logs and application logs using the Amazon CloudWatch Logs agent. Install the CloudWatch Logs agent on the on-premises servers. Transfer all logs from AWS to the on-premises data center. Use an Amazon Elasticsearch Logstash Kibana stack to analyze logs on premises.
D. Collect system logs and application logs by using the Amazon CloudWatch Logs agent. Install a CloudWatch Logs agent for on-premises resources. Store all logs in an S3 bucket in a central account.
Set up an Amazon S3 trigger and an AWS Lambda function to analyze incoming logs and automatically identify anomalies. Use Amazon Athena to run ad hoc queries on the logs in the central account.

Answer: C

NEW QUESTION # 134
......

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