How can you create 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)

In KQL the way to make a query reusable with different inputs is to define a stored function. A function lets you specify input parameters with types, embed the common query logic inside, and then call it from any KQL context with different arguments. This lets you centralize the exact filtering, joins, and projections you use repeatedly, so you don’t rewrite the same query and you can reuse it across dashboards, notebooks, or queries.

For example, you can create a tabular function that takes a region and a threshold as parameters and returns the matching rows. Once saved, you invoke it like a regular function by supplying the desired values. If you need to adjust the logic, you update the function in one place and every place that uses it automatically gets the new behavior. This approach is preferable to static precomputed results, like materialized views, or external scripts, when you want consistent, parameter-driven results directly within KQL.