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NEW QUESTION 50
Attempts, one of the three types of items associated with the schedule pipeline in the AWS Data Pipeline, provides robust data management.
Which of the following statements is NOT true about Attempts?

A. Attempts provide robust data management.B. AWS Data Pipeline retries a failed operation until the count of retries reaches the maximum number of allowed retry attempts.C. An AWS Data Pipeline Attempt object compiles the pipeline components to create a set of actionable instances.D. AWS Data Pipeline Attempt objects track the various attempts, results, and failure reasons if applicable.

Answer: C

Explanation:
Explanation
Attempts, one of the three types of items associated with a schedule pipeline in AWS Data Pipeline, provides robust data management. AWS Data Pipeline retries a failed operation. It continues to do so until the task reaches the maximum number of allowed retry attempts. Attempt objects track the various attempts, results, and failure reasons if applicable. Essentially, it is the instance with a counter. AWS Data Pipeline performs retries using the same resources from the previous attempts, such as Amazon EMR clusters and EC2 instances.
http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-how-tasks-scheduled.html

 

NEW QUESTION 51
A company is currently using AWS CodeCommit for its source control and AWS CodePipeline for continuous integration. The pipeline has a build stage for building the artifacts which is then staged in an Amazon S3 bucket.
The company has identified various improvement opportunities in the existing process, and a Solutions Architect has been given the following requirement:
* Create a new pipeline to support feature development
* Support feature development without impacting production applications
* Incorporate continuous testing with unit tests
* Isolate development and production artifacts
* Support the capability to merge tested code into production code.
How should the Solutions Architect achieve these requirements?

A. Trigger a separate pipeline from CodeCommit feature branches. Use AWS CodeBuild for running unit tests. Use CodeBuild to stage the artifacts within an S3 bucket in a separate testing account.B. Trigger a separate pipeline from CodeCommit tags Use Jenkins for running unit tests. Create a stage in the pipeline with S3 as the target for staging the artifacts with an S3 bucket in a separate testing account.C. Create a separate CodeCommit repository for feature development and use it to trigger the pipeline. Use AWS Lambda for running unit tests. Use AWS CodeBuild to stage the artifacts within different S3 buckets in the same production account.D. Trigger a separate pipeline from CodeCommit feature branches. Use AWS Lambda for running unit tests. Use AWS CodeDeploy to stage the artifacts within an S3 bucket in a separate testing account.

Answer: A

Explanation:
https://docs.aws.amazon.com/codebuild/latest/userguide/how-to-create-pipeline.html

 

NEW QUESTION 52
A company is processing videos in the AWS Cloud by using Amazon EC2 instances in an Auto Scaling group. It takes 30 minutes to process a video. Several EC2 instances scale in and out depending on the number of videos in an Amazon Simple Queue Service (Amazon SQS) queue.
The company has configured the SQS queue with a redrive policy that specifies a target dead-letter queue and a maxReceiveCount of 1. The company has set the visibility timeout for the SQS queue to 1 hour. The company has set up an Amazon CloudWatch alarm to notify the development team when there are messages in the dead-letter queue.
Several times during the day, the development team receives notification that messages are in the dead-letter queue and that videos have not been processed properly. An investigation finds no errors in the application logs.
How can the company solve this problem?

A. Turn on termination protection for the EC2 instances.B. Update the redrive policy and set maxReceiveCount to 0.C. Update the visibility timeout for the SOS queue to 3 hours.D. Configure scale-in protection for the instances during processing.

Answer: A

 

NEW QUESTION 53
A company has a media metadata extraction pipeline running on AWS. Notifications containing a reference to a file Amazon S3 are sent to an Amazon Simple Notification Service (Amazon SNS) topic The pipeline consists of a number of AWS Lambda functions that are subscribed to the SNS topic The Lambda functions extract the S3 file and write metadata to an Amazon RDS PostgreSQL DB instance.
Users report that updates to the metadata are sometimes stow to appear or are lost. During these times, the CPU utilization on the database is high and the number of failed Lambda invocations increases.
Which combination of actions should a solutions architect take to r-e'p resolve this issue? (Select TWO.)

A. Enable the RDS Data API for the RDS instance. Update the Lambda functions to connect to the RDS instance using the Data APIB. Create an Amazon Simple Queue Service (Amazon SOS) FIFO queue and subscribe the queue to the SNS topic Configure the Lambda functions to consume messages from the SQS queue.C. Create an Amazon Simple Queue Service (Amazon SQS) standard queue for each Lambda function and subscribe the queues to the SNS topic. Configure the Lambda functions to consume messages from their respective SQS queue.D. Enable massage delivery status on the SNS topic Configure the SNS topic delivery policy to enable retries with exponential backoffE. Create an RDS proxy for the RDS instance Update the Lambda functions to connect to the RDS instance using the proxy.

Answer: C,E

 

NEW QUESTION 54
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