You have been asked to de-risk deployments at your company. Specifically, the CEO is concerned about
outages that occur because of accidental inconsistencies between Staging and Production, which sometimes
cause unexpected behaviors in Production even when Staging tests pass. You already use Docker to get high
consistency between Staging and Production for the application environment on your EC2 instances. How do
you further de-risk the rest of the execution environment, since in AWS, there are many service components
you may use beyond EC2 virtual machines?

• A.Develop models of your entire cloud system in CloudFormation. Use this model in Staging and
  Production to achieve greater parity. */
• B.Use AWS Config to force the Staging and Production stacks to have configuration parity. Any
  differences will be detected for you so you are aware of risks.
• C.Use AMIs to ensure the whole machine, including the kernel of the virual machines, is consistent, since
  Docker uses Linux Container (LXC) technology, and we need to make sure the container environment is
  consistent.
• D.Use AWS ECS and Docker clustering. This will make sure that the AMIs and machine sizes are the
  same across both environments.

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A company is using AWS CodeCommit as its source code repository. After an internal audit, the compliance team mandates that any code change that go into the master branch must be committed by senior developers.
Which solution will meet these requirements?

• A.Create a repository in CodeCommit with a working and master branch. Create separate IAM groups for senior developers and developers. Use an IAM policy to assign each IAM group their corresponding branches. Once the code is merged to the working branch, senior developers can pull the changes from the working branch to the master branch.
• B.Create two repositories in CodeCommit: one for working and another for the master. Create separate IAM groups for senior developers and developers. Assign the resource-level permissions on the repositories tied to the IAM groups. After the code changes are reviewed, sync the approved files to the master code commit repository.
• C.Create a repository in CodeCommit. Create separate IAM groups for senior developers and developers. Assign code commit permissions for both groups, with code merge permissions for the senior developers group. Create a trigger to notify senior developers with a URL link to approve or deny commit requests delivered through Amazon SNS. Once a senior developer approves the code, the code gets merged to the master branch.
• D.Create a repository in CodeCommit. Create separate IAM groups for senior developers and developers. Use AWS Lambda triggers on the master branch and get the user name of the developer at the event object of the Lambda function. Validate the user name with the IAM group to approve or deny the commit.

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You are trying to debug the creation of Cloudformation stack resources. Which of the following can be used to help in the debugging process?
Choose 2 answers from the options below

• A.UseCloudtrail to debugall the API call's sent by the Cloudformation stack.
• B.Usethe AWS CloudFormation console to view the status of yourstack.
• C.Seethe logs in the/var/log directory for Linux instances
• D.UseAWSConfig to debug all the API call's sent by the Cloudformation stack.

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A company recently launched an application that is more popular than expected. The company wants to ensure the application can scale to meet increasing demands and provide reliability using multiple Availability Zones (AZs) The application runs on a fleet of Amazon EC2 instances behind an Application Load Balancer (ALB) A DevOps engineer has created an Auto Scaling group across multiple AZs for the application Instances launched in the newly added AZs are not receiving any traffic for the application.
What is likely causing this issue?

• A.Auto Scaling groups can create new instances in a single AZ only.
• B.The ALB should be replaced with a Network Load Balancer (NLB).
• C.The EC2 instances have not been manually associated to the ALB
• D.The new AZ has not been added to the ALB

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A web application has been deployed using an AWS Elastic Beanstalk application The Application Developers are concerned that they are seeing high latency in two different areas of the application:
- HTTP client requests to a third-party API
- MySQL client library queries to an Amazon RDS database
- A DevOps Engineer must gather trace data to diagnose the issues.
Which steps will gather the trace information with the LEAST amount of changes and performance impacts to the application?

• A.Add additional logging to the application code. Use the Amazon CloudWatch agent to stream the application logs into Amazon Elasticsearch Service. Query the log data in Amazon ES.
• B.On the AWS Elastic Beanstalk management page for the application, enable the AWS X-Ray daemon.
  View the trace data in the X-Ray console.
• C.Instrument the application to use the AWS X-Ray SDK. Post trace data to an Amazon Elasticsearch Service cluster. Query the trace data for calls to the HTTP client and the MySQL client.
• D.Instrument the application using the AWS X-Ray SDK. On the AWS Elastic Beanstalk management page for the application, enable the X-Ray daemon. View the trace data in the X-Ray console.

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