An Application team is refactoring one of its internal tools to run in AWS instead of on-premises hardware.
All of the code is currently written in Python and is standalone. There is also no external state store or relational database to be queried.
Which deployment pipeline incurs the LEAST amount of changes between development and production?

• A.Developers should use their native Python environment. When Dependencies are changed and a new container is ready, use AWS CodePipeline and AWS CodeBuild to perform functional tests and then upload the new container to the Amazon ECR. Use AWS CloudFormation with the custom container to deploy the new Amazon ECS.
• B.Developers should use their native Python environment. When Dependencies are changed and a new code is ready, use AWS CodePipeline and AWS CodeBuild to perform functional tests and then upload the new container to the Amazon ECR. Use CodePipeline and CodeBuild with the custom container to test new code changes inside AWS Elastic Beanstalk.
• C.Developers should use Docker for local development. Use AWS SMS to import these containers as AMIs for Amazon EC2 whenever dependencies are updated. Use AWS CodePipeline to test new code changes against the Auto Scaling group.
• D.Developers should use Docker for local development. When dependencies are changed and a new container is ready, use AWS CodePipeline and AWS CodeBuild to perform functional tests and then upload the new container to Amazon ECR. Use AWS CloudFormation with the custom container to deploy the new Amazon ECS.

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Your organization has decided to implement a third-party configuration management tool that uses a master server from which nodes pull configuration.
You have built a custom base Amazon Machine Image that already has the third-party configuration management agent installed.
You want to use the same base AMI in Development, Test and Production environments, each of which has its own master server.
How should you configure your Amazon EC2 instances to register with the correct master server on launch?

• A.Use Amazon CloudFormation to describe your environment.
  Configure an input parameter for the master server hostname/address, and use this parameter within an Amazon EC2 UserData script that registers the agent with the master.
• B.Use Amazon Simple Workflow Service to automate the process of registering new instances with your master server.
  Use an Environment tag in Amazon EC2 to register instances with the correct master server.
• C.Create a script on your third-party configuration management master server that queries the Amazon EC2 API for new instances and registers them with it.
• D.Create a tag for all instances that specifies their environment.
  When launching instances, provide an Amazon EC2 UserData script that gets this tag by querying the MetaData Service and registers the agent with the master.

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Which of the following can be configured as targets for Cloudwatch Events. Choose 3 answers from the options given below

• A.AmazonEC2 Instances
• B.AWSLambda Functions
• C.AmazonCodeCommit
• D.AmazonECS Tasks

Correct Answer: A,B,D

Explanation
The AWS Documentation mentions the below
You can configure the following AWS sen/ices as targets for Cloud Watch Events

For more information on Cloudwatch events please see the below link:
* http://docs.aws.amazon.com/AmazonCloudWatch/latest/events/WhatlsCloudWatchEvents.html

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A Developer is maintaining a fleet of 50 Amazon EC2 Linux servers. The servers are part of an Amazon EC2 Auto Scaling group, and also use Elastic Load Balancing for load balancing.
Occasionally, some application servers are being terminated after failing ELB HTTP health checks. The Developer would like to perform a root cause analysis on the issue, but before being able to access application logs, the server is terminated.
How can log collection be automated?

• A.Use Auto Scaling lifecycle hooks to put instances in a Pending:Wait state. Create an Amazon CloudWatch Alarm for EC2 Instance Terminate and trigger an AWS Lambda function that executes an SSM Run Command script to collect logs, push them to Amazon S3, and complete the Successful lifecycle action once logs are collected.
• B.Use Auto Scaling lifecycle hooks to put instances in a Terminating:Wait state. Create a Config rule for EC2 Instance-terminate Lifecycle and trigger a step function that executes a script to collect logs, push them to Amazon S3, and complete the lifecycle action once logs are collected. Action
• C.Use Auto Scaling lifecycle hooks to put instances in a Terminating:Wait state. Create an Amazon CloudWatch subscription filter for EC2 Instance and trigger a CloudWatch agent that executes a script to called logs, push them to Amazon S3, and complete the lifecycle action Terminate Successful once logs are collected.
• D.Use Auto Scaling lifecycle hooks to put instances in a Terminating:Wait state. Create an Amazon CloudWatch Events rule for EC2 Instance- and trigger an AWS Lambda function that executes a SSM Run Command script to collect logs, push them to Amazon S3, terminate Lifecycle Action and complete the lifecycle action once logs are collected.

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Your system uses a multi-master, multi-region DynamoDB configuration spanning two regions to achieve high availablity. For the first time since launching your system, one of the AWS Regions in which you operate over went down for 3 hours, and the failover worked correctly. However, after recovery, your users are experiencing strange bugs, in which users on different sides of the globe see different data. What is a likely design issue that was not accounted for when launching?

• A.The system does not have Lambda Functor Repair Automatons, to perform table scans and chack for corrupted partition blocks inside the Table in the recovered Region.
• B.The system did not implement DynamoDB Table Defragmentation for restoring partition performance in the Region that experienced an outage, so data is served stale.
• C.The system did not include repair logic and request replay buffering logic for post-failure, to re- synchronize data to the Region that was unavailable for a number of hours.
• D.The system did not use DynamoDB Consistent Read requests, so the requests in different areas are not utilizing consensus across Regions at runtime.

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