DAA-C01 Question 239
Single answerCompare and contrast different chart types (e.g., bar charts, scatter plots, heat grids, scorecards)A retail analytics team is building a Snowsight dashboard for regional sales managers. They need to satisfy four requirements using appropriate visualizations: (1) compare total revenue across product categories for the current quarter, (2) identify whether discount percentage is related to profit margin at the transaction level, (3) highlight days and stores with unusually high return rates across a large matrix of values, and (4) display month-to-date revenue against target as a single KPI for executives. Which combination of chart types is the BEST fit for these requirements?
- A
Bar chart for category revenue, scatter plot for discount vs. profit margin, heat grid for return rates by day and store, and scorecard for month-to-date revenue vs. target
- B
Scatter plot for category revenue, bar chart for discount vs. profit margin, scorecard for return rates by day and store, and heat grid for month-to-date revenue vs. target
- C
Heat grid for category revenue, scorecard for discount vs. profit margin, bar chart for return rates by day and store, and scatter plot for month-to-date revenue vs. target
- D
Scorecard for category revenue, heat grid for discount vs. profit margin, scatter plot for return rates by day and store, and bar chart for month-to-date revenue vs. target
Show answer and explanation
Correct answer: A
Explanation
The best answer is the combination in Option 1 because each chart type aligns with its strongest analytical use case. In Snowsight and BI best practices generally, bar charts are used to compare values across discrete categories; scatter plots are used to examine relationships, clustering, and outliers across two numeric measures; heat grids are used to visualize magnitude across a two-dimensional grid and identify hotspots; and scorecards are used to emphasize a single KPI or summary metric. The scenario tests whether the candidate can match business questions to the most effective visual encoding rather than simply recognizing chart names. This reflects practical dashboard design principles used in Snowflake analytics workflows: choose the visualization that maximizes interpretability for the specific analytical task and audience.
- A. Correct.
Correct. A bar chart is well suited for comparing aggregated values across discrete categories such as product categories. A scatter plot is the standard choice for assessing the relationship or correlation between two continuous numeric measures, such as discount percentage and profit margin at the transaction level. A heat grid is appropriate for scanning a dense two-dimensional matrix like day-by-store return rates and quickly spotting hotspots or anomalies by color intensity. A scorecard is designed to surface a single headline KPI, such as month-to-date revenue compared with a target, for executive consumption.
- B. Incorrect.
Incorrect. This option reverses the natural use of scatter plots and bar charts. Scatter plots are not the best way to compare a small set of aggregated category totals because category names are discrete dimensions, not continuous paired measures. A bar chart is also not appropriate for showing the relationship between two transaction-level continuous variables. A scorecard cannot effectively represent a matrix of return rates across both days and stores because it is intended for one summarized KPI. A heat grid is also not the best primary choice for a single KPI against target.
- C. Incorrect.
Incorrect. A heat grid can show category revenue, but it is typically less effective than a bar chart for straightforward comparison of a small number of category totals. A scorecard cannot show the relationship between discount percentage and profit margin because it summarizes one metric rather than visualizing paired observations. A bar chart is not the best tool for a dense day-by-store matrix where color-based scanning is needed. A scatter plot is also not appropriate for communicating a single KPI versus target to executives.
- D. Incorrect.
Incorrect. A scorecard is too limited for comparing multiple product categories because it emphasizes a single value or small set of KPI values rather than category-by-category comparison. A heat grid is not the best default for evaluating the relationship between two continuous measures at the transaction level; scatter plots are more appropriate because they preserve individual observations and patterns. A scatter plot is also not ideal for a day-by-store matrix of return rates, where a heat grid is better for identifying concentration and outliers. A bar chart can show progress toward target, but for a single executive KPI, a scorecard is typically the more direct and concise choice.