DAA-C01 Question 238
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 have four requirements for different tiles on the same dashboard: (1) quickly show current month revenue versus target as a single KPI, (2) compare total sales across product categories for the current quarter, (3) identify whether discount percentage is related to gross margin across thousands of transactions, and (4) spot periods of unusually high order volume by day of week and hour of day. Which combination of chart types is the MOST appropriate to satisfy these requirements?
- A
Use a scorecard for (1), a bar chart for (2), a scatter plot for (3), and a heat grid for (4).
- B
Use a heat grid for (1), a scorecard for (2), a bar chart for (3), and a scatter plot for (4).
- C
Use a scatter plot for (1), a heat grid for (2), a scorecard for (3), and a bar chart for (4).
- D
Use a scorecard for (1), a scatter plot for (2), a heat grid for (3), and a bar chart for (4).
Show answer and explanation
Correct answer: A
Explanation
This question tests the practical ability to match business questions to the most effective visualization type in a Snowflake analytics workflow such as Snowsight dashboards. Best practice is to choose the chart based on the analytical task: scorecards for a single KPI or status metric, bar charts for comparison across discrete categories, scatter plots for relationships between two continuous variables, and heat grids for identifying intensity or concentration across a two-dimensional matrix. These choices align with common BI and dashboard design principles used in Snowsight and other analytics tools: use the simplest visual that directly answers the question, minimize cognitive load, and avoid forcing users to interpret a chart type that does not match the data structure. In practice, selecting the right chart improves time-to-insight for dashboard consumers and reduces the chance of misinterpretation.
- A. Correct.
Correct. A scorecard is appropriate for a single headline KPI such as current month revenue versus target because it emphasizes one metric and often supports comparison to a prior value or goal. A bar chart is well suited for comparing aggregated values across discrete categories like product categories. A scatter plot is the standard choice for exploring the relationship or correlation between two continuous measures, such as discount percentage and gross margin, especially across many transactions. A heat grid is appropriate when the goal is to detect intensity patterns across two dimensions such as day of week and hour of day.
- B. Incorrect.
Incorrect. This option mismatches each requirement to a less suitable visualization. A heat grid is not the best choice for a single KPI because it is intended to show magnitude across a matrix of values, not one headline metric. A scorecard is also not appropriate for comparing multiple product categories at once. A bar chart does not show the relationship between two continuous measures as effectively as a scatter plot. A scatter plot is also a poor fit for day-of-week by hour-of-day pattern detection because the matrix structure is more naturally shown as a heat grid.
- C. Incorrect.
Incorrect. A scatter plot is not the right primary visual for a single KPI because the user would have to infer a value from plotted points rather than immediately reading a headline number. A heat grid is generally not the best first choice for comparing a small number of discrete category totals, where bar charts provide clearer ranked comparison. A scorecard cannot meaningfully show the relationship between discount percentage and gross margin across thousands of records because it collapses detail into a single value. A bar chart for hour by day patterns can work in limited cases, but it is much less effective than a heat grid for spotting concentration and outliers across two categorical time dimensions.
- D. Incorrect.
Incorrect. The first mapping for the KPI is reasonable, but the remaining choices are not. A scatter plot is not ideal for comparing totals across product categories because categories are discrete and usually better compared with bar length. A heat grid is not the best default for examining correlation between two continuous measures such as discount percentage and gross margin; a scatter plot shows clustering, trend, and outliers more directly. A bar chart for day-of-week by hour-of-day introduces too many grouped values and makes pattern detection harder than a heat grid.