DEA-C01 exam dumps

DEA-C01 practice question 90 of 550

AWS Certified Data Engineer - Associate. Associate level, Amazon Web Services. Free question with the correct answer and a full explanation.

DEA-C01 Question 90

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You are tasked with building an ETL pipeline that ingests raw log data into an S3 bucket, processes it to extract specific metrics, and loads the processed data into a Redshift data warehouse for analytical queries. The pipeline should handle large-scale data, support incremental data processing, and minimize operational overhead. Which combination of AWS services would best meet this requirement?

  1. A

    Amazon S3, AWS Glue, and Amazon Redshift

  2. B

    Amazon S3, AWS Lambda, and Amazon RDS

  3. C

    Amazon Kinesis Data Streams, AWS Glue, and Amazon Redshift

  4. D

    Amazon S3, Amazon EMR, and Amazon Redshift

  5. E

    Amazon DynamoDB, AWS Glue, and Amazon Redshift

Show answer and explanation

Correct answers: A, C

Explanation

Building scalable and cost-effective ETL pipelines requires choosing the right combination of services. Amazon S3 is ideal for storing raw data, AWS Glue provides a serverless ETL solution for data transformation, and Amazon Redshift enables efficient analytical workloads. For real-time data ingestion, Amazon Kinesis Data Streams is a suitable choice, while AWS Glue and Redshift ensure the pipeline can handle transformation and analytics, respectively. This combination minimizes operational overhead and meets the requirements of incremental data processing.

  • A. Correct.

    Correct. Amazon S3 is used for raw data storage, AWS Glue provides a serverless transformation service to process the data, and Amazon Redshift serves as the destination for analytics. This combination is cost-effective and scalable.

  • B. Incorrect.

    Incorrect. While Amazon S3 and AWS Lambda can be used for data processing, Amazon RDS is not designed for large-scale analytical workloads like Amazon Redshift.

  • C. Correct.

    Correct. Amazon Kinesis Data Streams can be used for real-time data ingestion, AWS Glue for data transformations, and Amazon Redshift for analytical queries. This setup is suitable for pipelines requiring real-time or near-real-time processing.

  • D. Incorrect.

    Incorrect. Amazon EMR can process data at scale, but it requires more operational management compared to AWS Glue. For a serverless solution with minimal overhead, AWS Glue is a better choice.

  • E. Incorrect.

    Incorrect. Amazon DynamoDB is a NoSQL database and not suitable for ETL pipelines designed to process and analyze large-scale data. It is not a replacement for Amazon S3 or Redshift in this scenario.

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