Exam AWS Certified Machine Learning - Specialty topic 1 question 31 discussion

Answer is B. Athena is best in Parquet format.

You can improve the performance of your query by compressing, partitioning, or converting your data into columnar formats. Amazon Athena supports open source columnar data formats such as Apache Parquet and Apache ORC. Converting your data into a compressed, columnar format lowers your cost and improves query performance by enabling Athena to scan less data from S3 when executing your query

Amazon Athena performs best when querying columnar storage formats like Apache Parquet. Given that 1 TB of data is generated every minute, optimizing storage format is critical for query performance and cost efficiency. Why Parquet (B) is the Best Choice? Columnar Storage: Parquet stores data by columns instead of rows, allowing Athena to scan only the needed columns, reducing the amount of data read. Compression Efficiency: Parquet automatically compresses data more efficiently than CSV or JSON. Smaller file sizes = Faster queries + Lower costs. Efficient Query Performance: Parquet supports predicate pushdown, meaning queries can skip irrelevant rows without scanning the entire dataset. Optimized for Big Data & Athena: Designed for big data workloads in Athena, Redshift Spectrum, and Presto. Works well with S3 partitioning to improve query speed.

A. NO - slower B. YES - Parquet native in Aethena/Presto C. NO - Compressed JSON D. NO - no built-in support

according to: https://dzone.com/articles/how-to-be-a-hero-with-powerful-parquet-google-and the query run time over parquet file was 6.78 seconds while it was 236 seconds on the same data but stored on csv file which mean that parquet file is 34x faster than csv file

B it is

Answer is B. https://aws.amazon.com/tw/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/ But why does this question relate to Machine Learning?