DAA-C01 Question 109
Single answerTop InsightsA retail analytics team uses Snowsight dashboards to monitor daily sales by region, channel, and product category. Executives want a dashboard tile that automatically highlights the most significant drivers behind sudden changes in sales without requiring analysts to manually build drill-down queries each time. The team wants to use Top Insights so business users can quickly identify what changed and why. Which approach best meets this requirement?
- A
Create a Top Insights tile on the dashboard based on a query result that includes a metric and relevant dimensions so Snowsight can automatically surface the largest contributors to change.
- B
Use a standard line chart with a forecast overlay, because Top Insights only works for time-series prediction and anomaly scoring.
- C
Build a dynamic table that precomputes all possible drill-down combinations, because Top Insights cannot analyze dimensional contributors from dashboard data.
- D
Enable Search Optimization Service on the sales table, because Top Insights requires search access paths before it can explain changes in metrics.
Show answer and explanation
Correct answer: A
Explanation
The best answer is to create a Top Insights tile from a query result that includes the target metric and relevant categorical dimensions. In Snowsight, Top Insights is intended to help users quickly understand major contributors to change in a measure by examining dimensional slices of the data. This aligns directly with the executive requirement to explain sudden changes in sales without building manual drill-down queries each time. The other options reflect common misconceptions: forecasting visuals are not the same as explanatory insights, dynamic tables are not required to make Top Insights work, and Search Optimization Service is unrelated to the core Top Insights capability. As a best practice, design the underlying query so it returns a meaningful metric and business-relevant dimensions that users would expect to compare when investigating change.
- A. Correct.
Correct. Top Insights in Snowsight is designed to help users understand key drivers behind changes in a metric by analyzing dimensional breakdowns in query results. In this scenario, a query that returns the business metric such as sales along with meaningful dimensions such as region, channel, and product category is the right input for a Top Insights visualization or tile. This supports self-service investigation of what changed and why, without manually authoring multiple drill-down queries.
- B. Incorrect.
Incorrect. This confuses Top Insights with forecasting or anomaly-detection style analytics. A line chart with forecast overlay may help visualize trends, but it does not automatically explain which dimensions contributed most to the observed change. Top Insights focuses on explanatory breakdowns, not predictive modeling.
- C. Incorrect.
Incorrect. Although precomputing combinations in a dynamic table might support some downstream analytics, it is not the best approach for this requirement and is not necessary to use Top Insights. The misconception is that all drill paths must be materialized ahead of time. Top Insights is intended to analyze the query result's metric and dimensions to identify important contributors without requiring analysts to manually enumerate every combination.
- D. Incorrect.
Incorrect. Search Optimization Service can improve selective query performance for certain access patterns, but it is not a prerequisite for Top Insights. The misconception is treating a performance feature as a functional dependency. Top Insights depends on appropriate metric-and-dimension query results, not on search optimization being enabled.