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.
Suggested Answer:C🗳️
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. Reference: http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-how-tasks-scheduled.html
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.
C is correct
Agree with C.
Instances — When AWS Data Pipeline runs a pipeline, it compiles the pipeline components to create a set of actionable instances. Each instance contains all the information for performing a specific task. The complete set of instances is the to-do list of the pipeline. AWS Data Pipeline hands the instances out to task runners to process.
https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-how-tasks-scheduled.html
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