A data analyst wants to save a newly analyzed data set to a local storage...

Given the requirements:

Minimized file size

Fast ingestion

Schema preservation (including data types)

The most appropriate format is:

→ Parquet – It is a columnar storage file format developed for efficient data processing. Parquet files are compressed, support schema embedding, and enable fast columnar reads, making them ideal for analytical workloads and big data environments.

Why the other options are incorrect:

A. JSON: Text-heavy and lacks native support for data types/schema.

C. XML: Verbose and has poor performance in storage and ingestion speed.

D. CSV: Flat structure, doesn’t store data types or schema, and can be large in size.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 6.2 (Data Storage Formats):“Parquet is a preferred format for data analysis as it provides efficient compression and encoding with embedded schema information, making it ideal for minimal storage and fast ingestion.”

Apache Parquet Documentation:“Parquet is designed for efficient data storage and retrieval. It includes schema support and works best for analytics use cases.”

Parquet is a columnar storage format that automatically includes schema (data types), uses efficient compression to minimize file size, and enables very fast reads for analytic workloads.