PL-300 Visualize and analyze the data Practice Question
You need to design a Power BI report that displays sales trend over time. The data includes dates with missing values for weekends and holidays. Which visualization type and approach should you use to ensure accurate trend representation?
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
✓
Line chart with a continuous date axis
A line chart with a continuous date axis (option C) is correct because a continuous axis plots dates proportionally along the X-axis, so gaps for weekends and holidays are preserved as empty intervals rather than being collapsed, giving an accurate time-based trend. Line charts are also the appropriate visual for showing trends over time, and the continuous axis correctly reflects the true temporal spacing between data points. Option A is wrong because a categorical date axis treats each date as an evenly spaced label, which distorts the time scale and hides gaps. Options B and D are wrong because bar charts are suited to comparing discrete categories rather than depicting continuous trends over time, regardless of axis type.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Line chart with a categorical date axis
Why it's wrong here
In Power BI, using a categorical date axis on a line chart treats each date as a discrete text-like category rather than a continuous time value. As a result, actual time intervals between dates are ignored, and missing dates are simply skipped, so the line connects across those gaps and implies a trend that is not supported by real data. Categorical axes also lack the chronological scaling needed for accurate sales trend analysis because equal spacing is given to every category, regardless of how much time has passed between data points.
- ✗
Bar chart with a categorical date axis
Why it's wrong here
A bar chart with a categorical date axis converts dates into independent categories, producing one bar per date value and completely losing the continuous nature of time. This axis type does not respect chronological ordering beyond simple sorting, nor does it account for missing dates or irregular intervals—bars appear at fixed spacing, which distorts the timeline and can mislead comparisons. Moreover, bar charts emphasize magnitude comparisons rather than direction or rate of change, making them inherently weaker than line charts for conveying a sales trend over time.
- ✓
Line chart with a continuous date axis
Why this is correct
A line chart with a continuous date axis is the correct choice for displaying sales trends because Power BI preserves the date data type and plots each point based on its true chronological position on a time-scaled axis. With a continuous axis, missing dates are rendered as genuine gaps in the line instead of falsely connecting across them, accurately reflecting periods with no recorded sales. This configuration also supports time intelligence features, handles uneven intervals correctly, and lets viewers perceive the slope and direction of sales movement—exactly what a trend visual requires.
- ✗
Bar chart with a continuous date axis
Why it's wrong here
Even though a continuous date axis on a bar chart correctly orders dates and leaves gaps for missing dates, the visual result is often misleading because each bar occupies a block of time and empty gaps can be misinterpreted as zero sales or as actual periods of inactivity. Bar charts are optimized for comparing discrete values, not for emphasizing continuous flow, and the eye must compare bar heights across uneven empty spaces, which obscures the underlying trend. This makes a bar chart with a continuous axis a poor substitute for a line chart when the goal is to analyze sales trends over time.
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