ARA-C01 Question 361
Single answerFunctionsA data engineering team has a table with semi-structured event data stored in a VARIANT column named PAYLOAD. Analysts frequently query nested attributes such as customerId and orderTotal, but the JSON structure varies across event types and some keys are missing. The architects want to standardize access to these values so analysts can reuse the same logic across reports while minimizing query errors when fields are absent or have the wrong type. Which approach should the architect recommend?
- A
Create SQL scalar UDFs that extract the nested attributes from PAYLOAD and use safe conversion logic such as TRY_TO_NUMBER where needed.
- B
Create a table UDF for each attribute so analysts can call it anywhere a scalar expression is expected in SELECT and WHERE clauses.
- C
Create a JavaScript stored procedure that returns the extracted values and call it inline from analyst queries.
- D
Replace the VARIANT column with VARCHAR and use string functions so all event structures can be handled consistently without JSON parsing.
Show answer and explanation
Correct answer: A
Explanation
The best answer is to create SQL scalar UDFs that encapsulate extraction and type-handling logic for commonly used attributes in semi-structured data. In Snowflake, scalar UDFs are intended for reusable logic that returns one value and can be used in SQL expressions. This makes them a strong fit for standardizing access to nested JSON fields in a VARIANT column. Combining path extraction with TRY_* conversion functions is a best practice when source data quality varies, because failed conversions return NULL rather than raising an error. By contrast, table UDFs return tabular results and are used in the FROM clause, so they are not appropriate for simple attribute extraction. Stored procedures are for procedural execution and cannot be embedded inline as scalar query expressions. Snowflake documentation on user-defined functions distinguishes scalar UDFs from table functions, and documentation on semi-structured data and conversion functions supports using VARIANT navigation plus TRY_TO_* functions for resilient querying.
- A. Correct.
Correct. A SQL scalar UDF is appropriate when the goal is to encapsulate reusable expression logic that returns a single value per row. For semi-structured data in VARIANT, the UDF can standardize path extraction and combine it with safe casts such as TRY_TO_NUMBER or TRY_TO_DECIMAL so queries return NULL instead of failing when a field is missing or malformed. This is well aligned with analyst-facing reuse in SELECT lists, filters, and transformations.
- B. Incorrect.
Incorrect. A table UDF returns a table result set, not a scalar value. It is useful when one input row should produce multiple output rows or columns in a FROM clause context. It cannot be used as a drop-in replacement for a scalar expression in places where analysts need a single extracted attribute such as customerId or orderTotal.
- C. Incorrect.
Incorrect. Stored procedures are designed for procedural orchestration and administrative or multi-step operations, not inline row-by-row expression evaluation inside SQL queries. Analysts cannot use a stored procedure call as a scalar expression in a standard SELECT or WHERE clause to extract values from each row.
- D. Incorrect.
Incorrect. Converting VARIANT to VARCHAR removes native semi-structured querying advantages and makes extraction less reliable, not more. Snowflake supports direct navigation of JSON structures in VARIANT, and string parsing would increase complexity and fragility. It also does not address safe typing as effectively as using UDFs with TRY_* conversion functions.