Which option describes creating a reusable parameterized query for a KQL database?

• A. Create a stored function.
• B. Create a materialized view
• C. Write a custom Python script and save it as a Spark job definition
• D. Define a parameter in a query profile

Answer: (A)

Using a stored function in KQL to define a parameterized query lets you pass in arguments and reuse the query logic across queries and dashboards. A function encapsulates the query with parameters, so you can call it with different values (like dates, region, or thresholds) without rewriting the whole query each time. This promotes reuse, consistency, and easier maintenance since any update to the logic happens in one place.

Materialized views cache results for performance but aren’t designed for parameterized reuse; they produce a fixed result set from a baseline query and aren’t invoked with different input values. A custom Python script saved as a Spark job is a separate processing task outside KQL. Defining a parameter in a query profile relates to how queries are run or managed rather than creating a reusable, parameterized KQL unit. Therefore, creating a stored function best matches the idea of a reusable parameterized KQL query.