Databricks Certified Data Engineer - Associate

Explanation

Correct Answer: C

When comparing Parquet vs CSV for creating external tables with CREATE TABLE AS SELECT (CTAS), the key benefit of Parquet is that it has a well-defined schema. Here's why:

1. Schema Definition: Parquet files store schema information (data types, column names, etc.) within the file itself, whereas CSV files are schema-less text files. When creating an external table from CSV, you often need to explicitly define the schema or rely on inference, which can be error-prone.

2. CTAS Operations: With CTAS statements, Parquet's inherent schema makes it easier to create tables because the schema is automatically detected and applied, reducing the need for manual schema specification.

3. Other Options Analysis:

   • A: Both Parquet and CSV files can be partitioned, so this is not a unique benefit of Parquet.
   • B: This is incorrect - CTAS statements can indeed be used on files.
   • D: While Parquet files can be optimized, this is more relevant to Delta Lake features rather than a direct benefit of Parquet over CSV for CTAS operations.
   • E: Parquet files don't automatically become Delta tables; they need to be converted to Delta format.

4. Practical Implication: When working with external tables in Databricks, using Parquet format simplifies schema management and ensures data type consistency, which is particularly valuable in data engineering workflows where schema evolution and data quality are important considerations.