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QUESTION NO: 1
A company has developed an AWS Lambda function that handles orders received through an
API. The company is using AWS CodeDeploy to deploy the Lambda function as the final stage of a
CI/CD pipeline. A DevOps Engineer has notices there are intermittent failures of the ordering API for a few seconds after deployment. After some investigation, the DevOps Engineer believes the failures are due to database changes the CloudFormation stack for the application lambda function begins executing. How should the DevOps Engineer overcome this?
A. Add a BeforeAllowTraffic hook to the AppSpec file that tests and waits for any necessary database changes before traffic can flow to the new version of the Lambda function
B. Add an AfterAllowTraffic hook to the AppSpec file that forces traffic to wait for any pending database changes before allowing the new version of the Lambda function to respond
C. Add a ValidateService hook to the AppSpec file that inspects incoming traffic and rejects the payload if dependent services such as the database are not yet ready
D. Add a BeforeInstall hook to the AppSpec file that tests and waits for any necessary database changes before deploying the new version of the Lambda function
Answer: B

QUESTION NO: 2
A DevOps Engineer manages a web application that runs on Amazon EC2 instances behind an
Application Load Balancer (ALB). The instances run in an EC2 Auto Scaling group across multiple
Availability Zones.
The Engineer needs to implement a deployment strategy that:
Launches a second fleet of instances with the same capacity as the original fleet.
Maintains the original fleet unchanged while the second fleet is launched.
Transitions traffic to the second fleet when the second fleet is fully deployed.
Terminates the original fleet automatically 1 hour after transition.
Which solution will satisfy these requirements?
A. Use AWS Elastic Beanstalk with the configuration set to Immutable. Create an .ebextension using the Resources key that sets the deletion policy of the ALB to 1 hour, and deploy the application.
B. Use an AWS CloudFormation template with a retention policy for the ALB set to 1 hour. Update the Amazon Route 53 record to reflect the new ALB.
C. Use AWS CodeDeploy with a deployment group configured with a blue/green deployment configuration. Select the option Terminate the original instances in the deployment group with a waiting period of 1 hour.
D. Use two AWS Elastic Beanstalk environments to perform a blue/green deployment from the original environment to the new one. Create an application version lifecycle policy to terminate the original environment in 1 hour.
Answer: D

QUESTION NO: 3
A Security team is concerned that a Developer can unintentionally attach an Elastic IP address to an Amazon EC2 instance in production. No Developer should be allowed to attach an Elastic IP address to an instance.
The Security team must be notified if any production server has an Elastic IP address at any time.
How can this task be automated?
A. Ensure that all IAM groups are associated with Developers do not have associate-address permissions.
Create a scheduled AWS Lambda function to check whether an Elastic IP address is associated with any instance tagged as production, and alert the Security team if an instance has an Elastic IP address associated with it.
B. Create an AWS Config rule to check that all production instances have the EC2 IAM roles that include deny associate-address permissions. Verify whether there is an Elastic IP address associated with any instance, and alert the Security team if an instance has an Elastic IP address associated with it.
C. Use Amazon Athena to query AWS CloudTrail logs to check for any associate-address attempts.
Create an AWS Lambda function to dissociate the Elastic IP address from the instance, and alert the
Security team.
D. Attach an IAM policy to the Developer's IAM group to deny associate-address permissions. Create a custom AWS Config rule to check whether an Elastic IP address is associated with any instance tagged as production, and alert the Security team.
Answer: D

QUESTION NO: 4
A company has an application that has predictable peak traffic times. The company wants the application instances to scale up only during the peak times. The application stores state in Amazon
DynamoDB. The application environment uses a standard Node.js application stack and custom Chef recipes stored in a private Git repository.
Which solution is MOST cost-effective and requires the LEAST amount of management overhead when performing rolling updates of the application environment?
A. Configure AWS OpsWorks stacks and push the custom recipes to an Amazon S3 bucket and configure custom recipes to point to the S3 bucket. Then add an application layer type for a standard
Node.js application server and configure the custom recipe to deploy the application in the deploy step from the S3 bucket. Configure time-based instances and attach an Amazon EC2 IAM role that provides permission to access DynamoDB
B. Create a custom AMI with the Node.js environment and application stack using Chef recipes. Use the AMI in an Auto Scaling group and set up scheduled scaling for the required times, then set up an
Amazon EC2 IAM role that provides permission to access DynamoDB.
C. Create a Docker file that uses the Chef recipes for the application environment based on an official
Node.js Docker image. Create an Amazon ECS cluster and a service for the application environment, then create a task based on this Docker image. Use scheduled scaling to scale the containers at the appropriate times and attach a task-level IAM role that provides permission to access DynamoD
D. Configure AWS OpsWorks stacks and use custom Chef cookbooks. Add the Git repository information where the custom recipes are stored, and add a layer in OpsWorks for the Node.js application server.
Then configure the custom recipe to deploy the application in the deploy step. Configure time-based instances and attach an Amazon EC2 IAM role that provides permission to access DynamoDB.
Answer: A

QUESTION NO: 5
A company hosts parts of a Python-based application using AWS Elastic Beanstalk. An Elastic
Beanstalk CLI is being used to create and update the environments. The Operations team detected an increase in requests in one of the Elastic Beanstalk environments that caused downtime overnight.
The team noted that the policy used for AWS Auto Scaling is NetworkOut. Based on load testing metrics, the team determined that the application needs to scale CPU utilization to improve the resilience of the environments. The team wants to implement this across all environments automatically.
Following AWS recommendations, how should this automation be implemented?
A. Using ebextensions, create a custom resource that modifies the AWSEBAutoScalingScaleUpPolicy and AWSEBAutoScalingScaleDownPolicy resources to use CPUUtilization as a metric to scale for the
Auto Scaling group.
B. Using ebextensions, place a script within the files key and place it in
/opt/elasticbeanstalk/hooks/appdeploy/pre to perform an API call to modify the scaling metric to
CPUUtilization for the Auto Scaling configuration. Use leader_only to place this script in only the first instance launched within the environment.
C. Using ebextensions, place a command within the container_commands key to perform an API call to modify the scaling metric to CPUUtilization for the Auto Scaling configuration. Use leader_only to execute this command in only the first instance launched within the environment.
D. Using ebextensions, configure the option setting MeasureName to CPUUtilization within the aws:autoscaling:trigger namespace.
Answer: D

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Updated: May 28, 2022

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