Quick Answer
The correct answer is that you can view the distribution of values, identify data type inconsistencies, and detect column quality issues using the Column Profile feature in Power Query. This feature provides a statistical summary for a selected column, including value distribution charts, distinct counts, and a data type indicator that flags mismatches—such as numbers stored as text—by highlighting errors directly in the profile pane. On the Microsoft Power BI Data Analyst PL-300 exam, this tests your ability to perform data profiling before loading, often appearing in scenario-based questions where you must choose the three valid tasks from a list of five options. A common trap is confusing the Column Profile with the Column Distribution or Column Quality features, which are separate panes; remember that Profile gives you the full statistical breakdown, while Distribution shows only frequency and Quality shows validity percentages. To recall the three tasks, think of the mnemonic “DVD”: Distribution, Value errors, and Data type detection.
This PL-300 practice question tests your understanding of prepare the data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are profiling data in Power Query Editor. Which THREE tasks can you perform using the Column Profile feature?
Add a conditional column
Why wrong: This is a transformation, not profiling.
Identify data type issues
Profiling can show data type errors.
Count distinct values
Column Profiling includes distinct count.
View the distribution of values
Column Profile shows a bar chart of value distribution.
Replace values
Why wrong: Replace Values is a transformation.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Identify data type issues
The Column Profile feature in Power Query Editor provides a statistical summary of the data in a selected column, including value distribution, distinct counts, and data type detection. Option B is correct because the profile automatically highlights data type inconsistencies (e.g., numbers stored as text) by showing the 'Data Type' indicator and flagging errors, enabling you to identify and fix type issues before loading data.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Add a conditional column
Why it's wrong here
This is a transformation, not profiling.
Identify data type issues
Why this is correct
Profiling can show data type errors.
Related concept
Read the scenario before looking for a memorised answer.
Count distinct values
Why this is correct
Column Profiling includes distinct count.
Related concept
Read the scenario before looking for a memorised answer.
View the distribution of values
Why this is correct
Column Profile shows a bar chart of value distribution.
Related concept
Read the scenario before looking for a memorised answer.
Replace values
Why it's wrong here
Replace Values is a transformation.
Common exam traps
The trap here is that candidates confuse the Column Profile feature (a read-only profiling tool) with data transformation actions like adding columns or replacing values, leading them to select options that are actually performed in other parts of Power Query Editor.
Detailed technical explanation
The Column Profile feature uses the Power Query engine's internal data sampling (by default, the first 1000 rows) to compute metrics like value distribution, column quality (valid, error, empty percentages), and distinct counts. It is particularly useful for detecting data type mismatches in large datasets where a column may contain mixed types (e.g., numbers and text), as the profile will show a 'Data Type' warning and list the non-conforming values in the 'Value Distribution' pane, allowing you to drill into specific issues.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Prepare the data — This question tests Prepare the data — Read the scenario before looking for a memorised answer..
The correct answer is: Identify data type issues — The Column Profile feature in Power Query Editor provides a statistical summary of the data in a selected column, including value distribution, distinct counts, and data type detection. Option B is correct because the profile automatically highlights data type inconsistencies (e.g., numbers stored as text) by showing the 'Data Type' indicator and flagging errors, enabling you to identify and fix type issues before loading data.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This PL-300 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PL-300 exam.
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