Query acceleration in Azure Data Lake Storage Gen2 is a feature that allows for efficient querying of data directly from storage without requiring data movement. According to Microsoft's official documentation, query acceleration specifically supports CSV and JSON formatted data as input formats.

Why CSV and JSON are correct:

• CSV (Comma-Separated Values): This is a widely used, simple text format that is natively supported by query acceleration. It allows for efficient parsing and filtering of structured data directly from storage.
• JSON (JavaScript Object Notation): This semi-structured format is also directly supported, enabling query acceleration to parse and query JSON documents efficiently without requiring data transformation.

Why other formats are not supported:

• Apache Parquet: While Parquet is highly efficient for analytics due to its columnar storage and compression, query acceleration does not currently support Parquet files as input. Parquet files require specialized processing that is not part of the query acceleration feature set.
• XML: This format is not supported by query acceleration. XML parsing requires more complex processing and is not included in the supported input formats.
• Avro: Although Avro is optimized for serialization and is efficient for big data workflows, it is not supported by query acceleration. The feature is specifically limited to CSV and JSON formats.

Query acceleration is designed to work with these specific text-based formats to provide fast, server-side filtering and projection capabilities, reducing the amount of data that needs to be transferred and processed by client applications.