SnowPro Associate: Platform Question 198
Single answer○ Structured dataA retail company loads daily product catalog files into a Snowflake table named PRODUCT_CATALOG. The source files contain consistent columns such as PRODUCT_ID, PRODUCT_NAME, CATEGORY, PRICE, and LOAD_DATE. Analysts frequently join this data to sales tables, filter on CATEGORY and LOAD_DATE, and require strong type enforcement for reporting accuracy. A data engineer is deciding whether to store the incoming data in a VARIANT column or in separate relational columns. Which approach best meets these requirements?
- A
Load the data into separate typed columns in PRODUCT_CATALOG because the schema is known and the data will be frequently filtered and joined
- B
Load the entire record into a single VARIANT column because VARIANT is always more performant than relational columns for analytical queries
- C
Load the data into a VARIANT column because structured data cannot be stored efficiently in standard Snowflake tables
- D
Load the data into separate VARCHAR columns only, because Snowflake does not support numeric or date data types for loaded files
Show answer and explanation
Correct answer: A
Explanation
For SnowPro Associate, candidates should recognize when to use standard relational columns versus semi-structured storage. If data is structured, has a predictable schema, and is regularly queried using joins, filters, and aggregations, the recommended approach is to load it into typed table columns. This improves usability, data quality, and query clarity. Snowflake's VARIANT data type is valuable for semi-structured or evolving schemas, but it is not the default best choice for stable structured datasets. This aligns with Snowflake documentation and best practices around loading structured data into standard table columns and using appropriate native data types for enforcement and analytics.
- A. Correct.
Correct. When the incoming data has a stable, well-defined schema and will be used in joins, filters, and reporting, storing it in separate relational columns with appropriate data types is the best choice. This supports type enforcement, simpler SQL, and efficient query processing for common analytical workloads. For structured data, Snowflake best practice is generally to model known attributes as standard table columns rather than placing everything in VARIANT.
- B. Incorrect.
Incorrect. VARIANT is designed for semi-structured data such as JSON, Avro, ORC, Parquet, or XML when schema flexibility is needed. It is not always more performant than relational columns. For known, consistently structured fields used in joins and predicates, standard typed columns are typically more appropriate and easier to govern.
- C. Incorrect.
Incorrect. Snowflake stores structured data very efficiently in regular relational tables. This option reflects a misconception that VARIANT is required for all ingestion use cases. In reality, Snowflake supports both structured and semi-structured data, and structured data with a known schema is commonly loaded into standard columns.
- D. Incorrect.
Incorrect. Snowflake fully supports a broad set of data types, including NUMBER, FLOAT, DATE, TIMESTAMP, BOOLEAN, and VARCHAR. Using only VARCHAR columns would reduce type safety and require repeated casting, which is not ideal for reporting accuracy or performance.