DEA-C01 · Question #263
A company adds new data to a large CSV file in an Amazon S3 bucket every day. The file contains company sales data from the previous 5 years. The file currently includes more than 5,000 rows. The CSV
The correct answer is B. Use prefixes to partition the data in the S3 bucket. Use the SALE_DATE column to create a. Partitioning the S3 data by SALE_DATE (daily prefixes) lets Athena prune non-matching partitions so it scans only the requested time range, minimizing bytes scanned and cost while working directly on the existing CSVs. Cataloging the partitions in AWS Glue enables efficient query
Question
A company adds new data to a large CSV file in an Amazon S3 bucket every day. The file contains company sales data from the previous 5 years. The file currently includes more than 5,000 rows. The CSV file structure is shown below with sample data:
The company needs to use Amazon Athena to run queries on the CSV file to fetch data from a specific time period. Which solution will meet this requirement MOST cost-effectively?
Exhibit
Options
- AWrite an Apache Spark script to convert the CSV data to JSON format. Create an AWS Glue job
- BUse prefixes to partition the data in the S3 bucket. Use the SALE_DATE column to create a
- CLaunch an Amazon EMR cluster. Specify AWS Glue Data Catalog as the default Apache Hive
- DCreate an Amazon RDS database. Create a table named SALES that matches the schema of the
How the community answered
(23 responses)- A4% (1)
- B74% (17)
- C9% (2)
- D13% (3)
Explanation
Partitioning the S3 data by SALE_DATE (daily prefixes) lets Athena prune non-matching partitions so it scans only the requested time range, minimizing bytes scanned and cost while working directly on the existing CSVs. Cataloging the partitions in AWS Glue enables efficient querying with minimal operational overhead.
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