Exam AWS Certified Data Engineer - Associate DEA-C01 topic 1 question 41 discussion

Exam question from Amazon's AWS Certified Data Engineer - Associate DEA-C01

Question #: 41
Topic #: 1

[All AWS Certified Data Engineer - Associate DEA-C01 Questions]

A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The .csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file.
Which solution will meet these requirements MOST cost-effectively?

A. Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.
B. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to ingest the data into the data lake in JSON format.
C. Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.
D. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to write the data into the data lake in Apache Parquet format.

Show Suggested Answer

Suggested Answer: D 🗳️

by atu1789 at Jan. 28, 2024, 9:39 p.m.

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imymoco

7 months, 2 weeks ago

Why not B? I think Athena also be able to handle json.

upvoted 1 times

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pypelyncar

1 year ago

Selected Answer: D

Athena is optimized for querying data stored in Parquet format. It can efficiently scan only the necessary columns for a specific query, reducing the amount of data processed. This translates to faster query execution times and lower query costs for data analysts who primarily focus on one or two columns

upvoted 2 times

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