A technology company has an application with millions of active users every day. The company queries daily usage data with Amazon Athena to understand how users interact with the application. The…

Question

Options

• AStore the data in Apache Avro format with the date and time as the partition, with the data sorted by the
• BStore the data in Apache Parquet format with the date and time as the partition, with the data sorted by
• CStore the data in Apache ORC format with the location ID as the partition, with the data sorted by the
• DStore the data in .csv format with the location ID as the partition, with the data sorted by the date and

• A
  15% (6)
• B
  75% (30)
• C
  8% (3)
• D
  3% (1)

Explanation

Apache Parquet with date/time partitioning is the optimal choice for Athena query performance. Parquet is a columnar storage format, meaning Athena only reads the specific columns requested rather than entire rows - dramatically reducing data scanned and lowering latency. Partitioning by date/time allows Athena to skip irrelevant partitions entirely for time-range queries. Sorting by location ID within partitions further improves predicate filtering. Apache Avro (A) is a row-based format, less efficient for analytical queries. ORC (C) is columnar but partitioning by location ID is less optimal for time-based analytics. CSV (D) is a row-based, uncompressed format with no columnar benefits, resulting in the highest latency and cost.

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