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?
⚠ Common exam trap
DA0-002 often tests the trade-off between data cleaning and information loss, tricking candidates into choosing options that exclude outliers or missing data rather than using visualization techniques to handle them.
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.
The analyst should use a line chart for the overall monthly sales trend to show the aggregated pattern over two years, and a separate bar chart for the top 5 stores to compare their performance. Tooltips can explain outlier values (e.g., Black Friday spikes) without distorting the overall trend. This approach handles missing data by aggregating (which can smooth over gaps) and addresses outliers by providing context rather than removing them, which could bias the analysis.
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
Excluding outliers discards genuine Black Friday promotional sales, distorting the trend the dashboard must show; missing months also break the line. Line charts are correct for continuous monthly trends, but the outlier removal, not the chart type, fails the accuracy requirement.
- ✗
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 drops entire stores from the comparison, so the top-5 ranking becomes incomplete and biased. Bar charts are appropriate for comparing discrete monthly totals, but removing affected stores rather than addressing the gaps fails the requirement to compare performance accurately.
- ✓
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
Aggregating daily sales into monthly totals smooths outage gaps, while a line chart shows trend and a bar chart ranks the top 5 stores. Tooltips flag promotional outliers without distorting the plotted values, satisfying both trend and comparison requirements.
- ✗
Use a stacked bar chart with all 50 stores, each bar representing a month.
Why it's wrong here
Stacking all 50 stores into monthly bars makes the top-5 comparison unreadable and the 10x promotional spikes compress every other store's values. Stacked bars suit part-to-whole composition across few categories; here they obscure trend and ranking, which the question requires.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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