DA0-002 Visualization and Reporting Practice Question
A retail company operates 50 stores across the country. The data analyst has been asked to create a dashboard to visualize monthly sales trends over the past two years and compare the performance of the top 5 stores. The dataset includes store name, date, and daily sales amount. Initial exploration reveals that some stores have missing sales data for certain months due to system outages, and there are occasional extreme values caused by promotional events (e.g., Black Friday sales are 10x normal). The analyst needs to choose an appropriate visualization approach that accurately represents the trends and comparisons while handling these data quality issues. What should the analyst do to best meet the requirements?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a line chart for the overall monthly sales trend (aggregated across all stores) and a separate bar chart for the top 5 stores by total sales, with tooltips explaining outlier values.
It combines a line chart for overall trend (aggregating all stores) and bar charts for top 5 stores, which allows comparison while handling missing data through aggregation. Tooltips can provide context for outliers. Option A is wrong because using separate line charts for each store becomes cluttered with 50 stores, and excluding outliers may omit valuable information about promotions. Option B is wrong because filtering out stores with missing data could remove important stores and distort the trend. Option D is wrong because a stacked bar chart with all 50 stores would be difficult to read and doesn't show trends over time effectively.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a line chart for each store's monthly sales and exclude outliers from the dataset.
Why it's wrong here
Too many lines cause clutter, and excluding outliers loses important promotional insights.
- ✗
Use a bar chart showing monthly sales for each month and filter out stores with missing data.
Why it's wrong here
Filtering out stores with missing data may remove key stores and skew the overall picture.
- ✓
Use a line chart for the overall monthly sales trend (aggregated across all stores) and a separate bar chart for the top 5 stores by total sales, with tooltips explaining outlier values.
Why this is correct
Combines trend and comparison effectively; aggregation handles missing data, and tooltips provide context.
- ✗
Use a stacked bar chart with all 50 stores, each bar representing a month.
Why it's wrong here
Stacked bar chart with 50 segments per bar is cluttered and not ideal for trend analysis.
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