SnowPro Associate: Platform Question 199
Single answer○ Structured dataA retail company stores order data in a Snowflake table named ORDERS with the following schema: ORDER_ID NUMBER, CUSTOMER_ID NUMBER, ORDER_DATE DATE, and TOTAL_AMOUNT NUMBER(12,2). Analysts frequently join ORDERS to a CUSTOMERS table on CUSTOMER_ID and filter ORDERS by ORDER_DATE ranges. Query performance has become inconsistent as data volume grows. The company wants to improve performance for these structured-data queries while minimizing ongoing maintenance. Which action is the most appropriate?
- A
Define a clustering key on (CUSTOMER_ID, ORDER_DATE) for the ORDERS table after confirming the table is large enough and query patterns are stable
- B
Convert the structured columns in ORDERS into a single VARIANT column so Snowflake can automatically optimize joins and date filtering
- C
Create a file format on the ORDERS table so Snowflake can prune rows more efficiently during joins
- D
Replace ORDER_DATE with a VARCHAR column because string comparisons are faster for date-range predicates in Snowflake
Show answer and explanation
Correct answer: A
Explanation
This question focuses on applying structured-data optimization practices in Snowflake. For relational tables with defined columns and predictable access patterns, Snowflake stores data in micro-partitions and can prune them during query execution. When pruning is not sufficient as data volume increases, defining a clustering key on columns commonly used in filters or joins can improve performance. However, clustering is not a blanket recommendation; it is most appropriate for large tables with stable query patterns because reclustering has compute implications. Using proper structured data types such as DATE and NUMBER is also a best practice, while VARIANT is designed for semi-structured data rather than replacing well-modeled relational columns. File formats are relevant to data loading and unloading, not runtime table optimization. These points align with Snowflake documentation on table clustering, micro-partitions, structured versus semi-structured data handling, and data loading concepts.
- A. Correct.
Correct. For large structured tables with frequent filtering and joins on specific columns, a clustering key can improve pruning and query performance by better organizing micro-partitions around those columns. In this scenario, CUSTOMER_ID and ORDER_DATE match the stated access patterns. Snowflake recommends considering clustering only when the table is sufficiently large and query patterns are consistent enough to justify the maintenance cost.
- B. Incorrect.
Incorrect. VARIANT is intended for semi-structured data such as JSON, Avro, or XML. Converting well-defined structured columns into VARIANT would typically reduce clarity and can make querying less straightforward. It does not provide an automatic performance advantage for relational joins and date-range predicates on already structured data.
- C. Incorrect.
Incorrect. File formats are used when loading or unloading data, such as defining CSV or JSON parsing rules for staged files. They do not affect how an existing table is physically organized for join performance or row pruning after the data is stored in Snowflake.
- D. Incorrect.
Incorrect. ORDER_DATE should remain a DATE data type for correct semantics and efficient date operations. Converting dates to VARCHAR is a common misconception; it can complicate filtering, sorting, and casting, and does not improve date-range query performance in Snowflake.