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%)
  --> Profile and clean the data
    --> Resolve inconsistencies, unexpected or null values, and data quality issues

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

1. Identifying Null Values

You open Power Query and want to quickly identify which columns contain null values. Which feature should you use?

A. Column distribution
B. Column quality
C. Column profile
D. Query dependencies

Correct Answer: B

Explanation:
Column Quality displays the percentage of Valid, Error, and Empty (null) values, making it the fastest way to identify nulls in a column.

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2. Replacing Null Values Appropriately

A numeric column contains null values that should be treated as zero for reporting purposes. What is the BEST approach?

A. Remove rows with null values
B. Replace null values with 0
C. Fill down the column
D. Change the column data type

Correct Answer: B

Explanation:
Replacing nulls with zero is appropriate when the business meaning supports it. Removing rows would result in data loss.

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3. Fixing Inconsistent Text Values

A column contains values such as “USA”, “usa”, and “Usa”. What is the MOST efficient way to standardize these values?

A. Replace values manually
B. Create a conditional column
C. Apply text formatting (Uppercase)
D. Remove duplicate rows

Correct Answer: C

Explanation:
Applying a text format such as Uppercase standardizes casing efficiently across the entire column.

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4. Detecting Unexpected Values

You suspect a column contains invalid numeric values outside the expected range. Which Power Query feature helps identify this?

A. Column quality
B. Column distribution
C. Column profile
D. Data type conversion

Correct Answer: C

Explanation:
Column Profile shows statistics such as minimum, maximum, and average, making it ideal for detecting outliers and unexpected values.

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5. Understanding Data Profiling Scope

Why might column profiling results appear inaccurate in Power Query?

A. Profiling only works for text columns
B. Profiling is disabled by default
C. Profiling is based on a data sample
D. Profiling ignores null values

Correct Answer: C

Explanation:
By default, Power Query profiles only a sample of rows. You must enable “Column profiling based on entire dataset” for full accuracy.

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6. Handling Error Values

A column contains error values caused by invalid data type conversions. What is the BEST first step?

A. Remove rows with errors
B. Replace errors with null
C. Fix the transformation causing the error
D. Change the column name

Correct Answer: C

Explanation:
Best practice is to address the root cause of errors rather than simply removing or replacing them.

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7. Removing Leading and Trailing Spaces

A text column appears to have duplicate values due to extra spaces. Which transformation should you apply?

A. Clean
B. Trim
C. Replace values
D. Capitalize Each Word

Correct Answer: B

Explanation:
Trim removes leading and trailing spaces, which commonly cause hidden inconsistencies in text data.

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8. Cleaning Non-Printable Characters

Some text values contain hidden characters that affect filtering and grouping. Which transformation should you use?

A. Trim
B. Replace errors
C. Clean
D. Format → Lowercase

Correct Answer: C

Explanation:
The Clean transformation removes non-printable characters that are not visible but can impact data quality.

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9. Resolving Missing Values in Hierarchical Data

A dataset contains a category value only in the first row of each group, with subsequent rows being null. What is the BEST solution?

A. Replace nulls with “Unknown”
B. Fill down
C. Remove null rows
D. Merge columns

Correct Answer: B

Explanation:
Fill Down propagates the previous non-null value and is commonly used for hierarchical or grouped data structures.

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10. Examining Category Inconsistencies

You want to identify inconsistent category names such as misspellings or variations. Which tool is MOST useful?

A. Column quality
B. Column distribution
C. Query parameters
D. Conditional formatting

Correct Answer: B

Explanation:
Column Distribution shows value frequency, making it easy to spot variations and inconsistencies in categorical data.

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✅ PL-300 Exam Takeaways

• Know which profiling tool answers which question
• Understand why a transformation is used, not just how
• Prefer fixing issues in Power Query instead of DAX
• Expect scenario-based questions asking for the best action

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