DAA-C01 Question 203
Single answerCreate a reusable filterA retail analytics team uses Snowsight dashboards to monitor daily sales. Several worksheets and dashboard tiles need to be filtered repeatedly to show only the current user's assigned region. The lead analyst wants a solution that can be defined once and reused across analyses, while still allowing other analysts to apply the same logic without rewriting the predicate each time. Which approach best meets this requirement?
- A
Create a view that hard-codes one region in the WHERE clause, and have all analysts query that view
- B
Create a reusable custom filter in Snowsight based on the region column and save it for repeated use across analyses
- C
Ask each analyst to add a worksheet-level filter manually whenever they open a new analysis
- D
Create a temporary table containing only one region's rows and point each dashboard tile to that table
Show answer and explanation
Correct answer: B
Explanation
The best answer is to create and save a reusable custom filter in Snowsight. Reusable filters are intended to support common, repeated filtering conditions so analysts do not have to redefine the same predicates across worksheets and dashboards. This aligns with analytics best practices: centralize repeated business logic where the tool supports it, reduce manual repetition, and improve consistency across visualizations. The other options either hard-code the logic in a way that reduces flexibility, rely on repetitive manual steps, or misuse physical data structures for an interactive analysis problem. In Snowflake documentation and Snowsight best practices, saved or reusable analytical components are preferred when the same filter logic needs to be applied consistently across multiple analyses.
- A. Incorrect.
Incorrect. A view with a hard-coded region is not flexible or reusable for multiple users with different assigned regions. It also requires changing the view definition or creating multiple views for different regions. While views are reusable database objects, this approach does not address reusable filtering logic in an analyst-friendly way within Snowsight.
- B. Correct.
Correct. A reusable custom filter in Snowsight is designed for this use case: define filter logic once and apply it repeatedly across analyses. This reduces duplication, improves consistency, and supports repeated analyst workflows without manually rebuilding the same filter each time.
- C. Incorrect.
Incorrect. Manually adding worksheet-level filters works functionally, but it does not create a reusable filter. It increases the chance of inconsistency and user error, especially when multiple worksheets and dashboard components must use the same logic.
- D. Incorrect.
Incorrect. A temporary table is session-scoped and not a practical reusable filtering mechanism for dashboards or shared analysis. It also duplicates data unnecessarily and creates maintenance overhead. Temporary tables are for transient data processing, not for reusable interactive filtering.