DAA-C01 Question 242
Single answerCreate charts and dashboard in SnowsightA retail analytics team uses Snowsight to monitor weekly sales performance. An analyst has already written a SQL query that returns one row per week with these columns: WEEK_START_DATE, REGION, TOTAL_SALES, and ORDER_COUNT. The VP wants a dashboard tile that clearly shows the sales trend over time and can be filtered by REGION without rewriting SQL each time. Which approach in Snowsight best meets this requirement?
- A
Create a line chart using WEEK_START_DATE on the x-axis and TOTAL_SALES on the y-axis, then add the chart to a dashboard and use a dashboard filter mapped to REGION.
- B
Create a KPI chart using TOTAL_SALES, then add REGION as a tooltip field because tooltip fields can be used as dashboard filters.
- C
Create a table visualization and rely on Snowsight users to sort WEEK_START_DATE descending, since tables provide the clearest time-series trend analysis in dashboards.
- D
Create a bar chart with REGION on the x-axis and TOTAL_SALES on the y-axis, then use WEEK_START_DATE as a dashboard filter to represent trend over time.
Show answer and explanation
Correct answer: A
Explanation
In Snowsight, the best practice for visualizing a metric over time is to use a line chart with a date or timestamp field on the x-axis and the measure on the y-axis. Dashboards are designed to combine such visualizations and support interactive filtering so business users do not need to modify SQL for common slice-and-dice analysis. In this scenario, REGION should be used as the dashboard filter because the query already returns weekly rows and the business requirement is to preserve the time trend while allowing regional selection. This aligns with Snowsight dashboard and charting guidance: choose a visualization type that matches the analytical question, then use dashboard filters for interactive exploration rather than redesigning the query or relying on less suitable visuals such as KPIs or tables for trend analysis.
- A. Correct.
Correct. For showing how a metric changes over time, a line chart is the most appropriate Snowsight visualization because it emphasizes trend across an ordered date axis. Using WEEK_START_DATE on the x-axis and TOTAL_SALES on the y-axis matches the VP's requirement to see weekly sales trend. Adding the chart to a dashboard and applying a dashboard filter for REGION allows consumers to interactively narrow results without editing the underlying SQL each time, which is a practical and standard Snowsight workflow.
- B. Incorrect.
Incorrect. A KPI visualization is intended to highlight a single aggregated value, not a trend across time. Although tooltip fields can add context to a chart, they do not serve as dashboard filter controls in the way required here. This option reflects a common misconception that adding a field to a visualization automatically makes it available as an interactive dashboard filter.
- C. Incorrect.
Incorrect. A table can display detailed data, but it is not the best choice for quickly communicating a trend over time to an executive audience. Requiring users to manually sort and interpret rows is less effective than a visual time-series chart. This distractor targets the misconception that any visual in a dashboard is equally suitable for trend analysis.
- D. Incorrect.
Incorrect. A bar chart by REGION compares categories, not time progression. Filtering on WEEK_START_DATE might narrow the dataset to a period, but it does not inherently show the sales trend over time. Because the VP specifically wants to see a weekly trend and filter by REGION, this design reverses the primary analytical need.