AWS Certified Data Analytics Specialty 2021 – Hands-On - AWS Data Pipeline

AWS Data Pipeline is a web service for scheduling and automating data workflows. It allows users to move and process data between AWS services like EC2, S3, RDS, DynamoDB, and Redshift. Data Pipeline supports task scheduling, dependency management, and automatic retries. It can also handle on-premises data through task runners. Key activities include running EMR jobs, executing Hive queries, and performing data copy operations. The service ensures reliability and flexibility in managing data workflows.

What is the primary function of AWS Data Pipeline?

• To provide real-time data analytics

• To secure data in transit

• To schedule and automate data workflows

• To store large amounts of data

Which AWS services can AWS Data Pipeline integrate with?

• Only S3 and EC2

• Only DynamoDB and S3

• S3, RDS, DynamoDB, Redshift, and EMR

• Only EMR and Redshift

What is a precondition check in AWS Data Pipeline?

• A tool for visualizing data

• A feature to delete old data

• A way to encrypt data

• A method to ensure data is ready before processing

How can AWS Data Pipeline handle on-premises data?

• By using a task runner installed on on-premises machines

• By directly connecting to on-premises databases

• By using AWS Direct Connect

• By uploading data manually to S3

What is the purpose of a task runner in AWS Data Pipeline?

• To execute tasks on AWS infrastructure

• To manage data encryption

• To run tasks on on-premises machines

• To visualize data flow

Which activity allows AWS Data Pipeline to execute Hive queries?

• EMR activity

• Copy activity

• Hive activity

• SQL query activity

What happens if an activity in AWS Data Pipeline fails after all retry attempts?

• It triggers an on-failure alarm

• It deletes the data

• It pauses the entire pipeline

• It automatically retries indefinitely