This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Prepare the data (25–30%)
   --> Transform and load the data
      --> Group and aggregate rows

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

Question 1

You have a sales table with one row per transaction. You need a table that shows total sales per customer, and this logic will not change. Where should you perform this aggregation?

A. Create a DAX measure using SUM()
B. Use Power Query Group By on Customer
C. Create a calculated column in DAX
D. Use a visual-level aggregation

Correct Answer: B

Explanation:
Power Query Group By is ideal for static aggregations that do not need to respond to slicers. This reduces data volume and improves performance, which aligns with PL-300 best practices.

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Question 2

Which aggregation option in Power Query counts the number of rows per group, regardless of column values?

A. Count
B. Count Distinct
C. Count Rows
D. Sum

Correct Answer: C

Explanation:
Count Rows counts all rows in each group. This is commonly tested and often confused with Count Distinct.

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Question 3

You group a fact table in Power Query by Product and Year. What is the resulting grain of the table?

A. One row per product
B. One row per year
C. One row per product and year
D. One row per transaction

Correct Answer: C

Explanation:
When grouping by multiple columns, the output grain is the unique combination of those columns.

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Question 4

What is a key impact on the data model when you group and aggregate rows in Power Query?

A. Measures become faster but less accurate
B. Relationships are automatically removed
C. Detailed transaction-level data is no longer available
D. DAX calculations are no longer required

Correct Answer: C

Explanation:
Grouping in Power Query permanently removes lower-level detail, which can limit drill-down and analytical flexibility.

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Question 5

Which scenario indicates you should NOT group data in Power Query?

A. The dataset is very large
B. Aggregation logic is fixed
C. Users need slicer-driven calculations
D. You want to reduce model size

Correct Answer: C

Explanation:
If calculations must respond dynamically to slicers or filters, aggregation should be handled with DAX measures, not Power Query grouping.

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Question 6

A Power BI report requires time intelligence calculations such as Year-to-Date sales. Where should aggregation occur?

A. Power Query Group By
B. Calculated columns
C. DAX measures
D. Visual-level filters

Correct Answer: C

Explanation:
Time intelligence requires dynamic context, which only DAX measures can provide. Grouping in Power Query would prevent proper time-based calculations.

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Question 7

What does the All Rows aggregation option produce in Power Query?

A. A single aggregated value
B. A calculated column
C. A nested table per group
D. A distinct count

Correct Answer: C

Explanation:
All Rows creates a nested table containing all rows for each group, often used for advanced transformations or custom calculations.

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Question 8

You mistakenly use Sum instead of Count Rows during a Group By operation. What is the most likely issue?

A. Incorrect totals due to data type mismatch
B. Missing relationships in the model
C. Duplicate rows created
D. Slower refresh times

Correct Answer: A

Explanation:
Using the wrong aggregation function can produce incorrect results, especially if the column contains non-additive values.

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Question 9

Which benefit is most directly associated with grouping data in Power Query?

A. Improved visual formatting
B. Reduced dataset size
C. Increased DAX flexibility
D. Automatic relationship creation

Correct Answer: B

Explanation:
Grouping reduces the number of rows loaded into the model, improving performance and memory usage.

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Question 10

Which statement best reflects a PL-300 best practice regarding grouping and aggregation?

A. Always aggregate data before loading
B. Group data only when DAX cannot be used
C. Group data when the required grain is known and fixed
D. Use grouping to replace all measures

Correct Answer: C

Explanation:
Grouping is appropriate when the required grain is known, stable, and does not require interactivity. Knowing when not to group is just as important for the exam.

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Final Exam Tips for This Topic

• Expect decision-based questions, not just “how-to”
• Be clear on Power Query vs DAX responsibilities
• Understand grain, performance, and flexibility trade-offs
• Watch for Count vs Count Rows vs Count Distinct
• Remember: Grouping is irreversible

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