This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Visualize and analyze the data (25–30%)
   --> Identify patterns and trends
      --> Detect outliers and anomalies

Below are 10 practice questions (with answers and explanations) for this topic of the exam.
There are also 2 practice tests for the PL-300 exam with 60 questions each (with answers) available on the hub.

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Practice Questions

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Question 1

You want Power BI to automatically identify unexpected spikes in daily sales over time. Which visual feature should you use?

A. Error bars
B. Reference lines
C. Anomaly detection on a line chart
D. Key Influencers visual

✅ Correct Answer: C

Explanation:
Anomaly detection is an AI-driven feature available on line charts that automatically identifies unusual values in time-series data.

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Question 2

Which requirement must be met to use Power BI’s built-in anomaly detection feature?

A. The dataset must be in Import mode
B. The visual must use time-based data
C. The model must include DAX measures
D. The data must be refreshed in real time

✅ Correct Answer: B

Explanation:
Anomaly detection works only with time-series data (such as dates or timestamps).

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Question 3

A data point appears far above the average line in a bar chart. What does this most likely indicate?

A. A guaranteed data error
B. An outlier that may require investigation
C. An automatically detected anomaly
D. A forecasting failure

✅ Correct Answer: B

Explanation:
Values far from an average reference line are potential outliers, but they are not automatically classified as errors.

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Question 4

Which Power BI feature helps you understand why an anomaly occurred by breaking a metric into contributing dimensions?

A. Key Influencers
B. Decomposition Tree
C. Error bars
D. Forecasting

✅ Correct Answer: B

Explanation:
The Decomposition Tree allows users to drill into dimensions (such as region or product) to explain anomalies.

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Question 5

Which visual is most effective for identifying outliers when comparing two numeric variables?

A. Line chart
B. Table
C. Scatter chart
D. Gauge

✅ Correct Answer: C

Explanation:
Scatter charts make extreme values stand out when comparing two numerical measures.

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Question 6

What information can Power BI display when an anomaly is detected on a line chart?

A. Only the anomalous data point
B. Expected value and confidence bounds
C. A DAX formula used to detect it
D. The exact root cause

✅ Correct Answer: B

Explanation:
Power BI shows the expected value, upper and lower bounds, and highlights the anomalous point.

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Question 7

Which statement about outliers is TRUE?

A. All outliers must be removed
B. Outliers are always caused by data errors
C. Outliers may represent meaningful business events
D. Power BI automatically corrects outliers

✅ Correct Answer: C

Explanation:
Outliers can indicate fraud, exceptional performance, or special events, not just data issues.

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Question 8

Which feature helps highlight data points that fall outside a defined range but does not automatically detect anomalies?

A. Anomaly detection
B. Error bars
C. AI insights
D. Smart narratives

✅ Correct Answer: B

Explanation:
Error bars show variation and uncertainty but do not automatically identify anomalies.

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Question 9

You want to highlight the top 5% of sales values to identify extreme performers. What should you use?

A. Median reference line
B. Forecasting
C. Percentile reference line
D. Anomaly detection

✅ Correct Answer: C

Explanation:
Percentile reference lines (such as the 95th percentile) help identify extreme high or low values.

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Question 10

Which limitation of Power BI anomaly detection should you be aware of for the PL-300 exam?

A. It requires Premium capacity
B. It only works with streaming datasets
C. It relies on historical patterns and trends
D. It requires manual DAX calculations

✅ Correct Answer: C

Explanation:
Anomaly detection is based on historical data patterns, meaning unusual external events may not be fully explained.

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Final Exam Tips

✔ Expect conceptual questions, not statistical formulas
✔ Know when to use anomaly detection vs visual analysis
✔ Understand that detection ≠ interpretation
✔ Focus on business context and storytelling

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