A company wants to optimize the cost of its data and analytics platform. The company is ingesting a number of .csv and JSON files in Amazon S3 from various data sources. Incoming data is expected to…

Question

Options

• AUse an AWS Glue ETL job to compress, partition, and convert the data into a columnar data
• BUse an AWS Glue ETL job to partition and convert the data into a row-based data format. Use
• CUse an AWS Glue ETL job to compress, partition, and convert the data into a columnar data
• DUse an AWS Glue ETL job to partition and convert the data into a row-based data format. Use

• A
  47% (14)
• B
  13% (4)
• C
  7% (2)
• D
  33% (10)

Explanation

Amazon Athena charges based on the amount of data scanned per query. To minimize cost and meet the <1 minute query SLA, the optimal strategy is to (1) convert raw CSV/JSON to a columnar format (like Parquet or ORC) using AWS Glue ETL - columnar formats allow Athena to skip irrelevant columns and compress data far more efficiently than row-based formats; (2) partition the data (e.g., by date) so Athena only scans the relevant partition rather than the entire dataset; and (3) apply S3 Lifecycle policies to transition data older than 5 years to a cheaper storage class (e.g., S3 Glacier) since it is infrequently queried, while still retaining it indefinitely for compliance. Options B and D are wrong because row-based formats (like CSV or Avro) do not support column pruning - Athena must scan entire rows, increasing cost and latency. Option C likely omits the lifecycle policy for aging data, making it more expensive for rarely accessed cold data.

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