The correct answers are A. JSON null values and E. Timestamps within strings .
Important correction:
The provided answer A, B is not the best answer. Snowflake documentation specifically calls out that certain values in semi-structured data may not be extracted into separate columnar storage, which can result in slower queries and increased storage usage. Two commonly tested examples are JSON null values and date or timestamp values stored as strings.
Why A is correct:
When a semi-structured element contains JSON null values, Snowflake might not extract that element into a separate column. This can reduce pruning efficiency and require Snowflake to scan more of the VARIANT structure during query execution.
Why E is correct:
Date and timestamp values inside semi-structured data that are stored as strings may not be optimized in the same way as native typed values. Storing timestamps as strings can increase storage and slow query performance because Snowflake may need to parse or cast the values at query time.
Why the other options are incorrect:
B). Floating-point numbers are supported inside VARIANT; they are not the main documented issue in this context.
C). Arrays are supported in semi-structured data. While very complex nested structures can affect query patterns, arrays alone are not the best answer here.
D). Vectors are a Snowflake data type used for vector operations and are not the standard answer for this semi- structured data optimization question.
Official Snowflake documentation reference:
Snowflake documentation explains that for semi-structured data, elements containing JSON null values may not be extracted into separate columns. It also recommends storing date and timestamp values in native typed columns when possible rather than as strings inside VARIANT, to improve query performance and storage efficiency.
Reference: Snowflake Documentation - Considerations for semi-structured data stored in VARIANT; Snowflake Documentation - Semi-structured data types; SnowPro Core Study Guide - Working with Semi- Structured Data.