COF-C03 Data Loading, Unloading, and Connectivity Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Parquet files are always smaller than CSV files regardless of the data content.

  Why it's wrong here

  While Parquet often provides better compression due to its columnar nature, it is not 'always' smaller than CSV. For very simple datasets with few columns and short strings, a gzipped CSV might occasionally be smaller. The primary advantage of Parquet in Snowflake is its structural awareness rather than a guaranteed smaller file size in every case.

• ✓

  Parquet files contain schema metadata, allowing for easier mapping of complex data.

  Why this is correct

  Because Parquet is self-describing, Snowflake can use functions like INFER_SCHEMA to automatically determine table structures. This reduces the manual effort required to define table columns and ensures that data types are preserved correctly from the source. It also supports nested structures like arrays and objects much more naturally than flat CSV files.

• ✗

  Parquet files can be loaded using the PUT command directly into a table.

  Why it's wrong here

  The PUT command is used to upload files from a local machine to a Snowflake stage, not to load data directly into a table. Regardless of the file format (Parquet, CSV, or JSON), the COPY INTO command is still required to move the data from the stage into the final Snowflake table structure.

• ✗

  Parquet is the only format that supports the ON_ERROR = CONTINUE parameter.

  Why it's wrong here

  The ON_ERROR parameter is supported for all major file formats in Snowflake, including CSV, JSON, XML, and Avro. It is not an exclusive feature of the Parquet format. Error handling logic is a function of the COPY INTO command itself, providing consistent behavior across different source data types during the ingestion process.