How can a Data Analyst automatically create a table structure for loading a Parquet file?

Manually defining table structures for complex semi-structured files like Parquet can be error-prone and time-consuming. Snowflake provides a specific automation workflow to handle this, involving the detection of the file's internal schema and the dynamic creation of a matching table.

The process starts with the INFER_SCHEMA function. Because Parquet files are self-describing, they contain metadata about their columns and data types. INFER_SCHEMA reads this metadata from files in a stage and returns a list of column names and types. To turn this list into an actual table, the analyst uses the CREATE TABLE ... USING TEMPLATE syntax. This command takes the output of INFER_SCHEMA as an input and automatically builds a table with the corresponding definition.

Evaluating the Options:

Option A is incorrect because CREATE TABLE LIKE is used to copy the structure of an existing table, not to build a new one from file metadata.

Option C and D are incorrect because GENERATE_COLUMN_DESCRIPTION is a helper function used to create a formatted string of column definitions, but it is not the primary command used with USING TEMPLATE for automated table creation.

Option B is the Correct answer. The combination of INFER_SCHEMA (to find the columns) and USING TEMPLATE (to build the table) is the standard Snowflake pattern for schema-on-read automation in Data Ingestion workflows.