Quick Answer
The answer is Change data type, Remove duplicates, and Replace values. These three transforms are correct because Power Query transforms clean data by directly addressing common data quality issues: changing data types ensures numerical and date fields are correctly formatted for calculations, removing duplicates eliminates redundant rows that could skew aggregations, and replacing values corrects inconsistencies like nulls or typos. On the Microsoft Power BI Data Analyst PL-300 exam, this question tests your understanding of the M language’s core cleaning functions within the Query Editor, often appearing in scenario-based items where you must identify the most appropriate transform for a given dirty dataset. A common trap is confusing “Merge queries” or “Group by” as cleaning steps—these are shaping transforms, not cleaning ones. Remember the mnemonic “CRR” for Clean: Change type, Remove duplicates, Replace values.
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.
Which THREE are types of Power Query transforms that can be used to clean data? (Choose three.)
Remove duplicates
Removing duplicates cleans the data by eliminating redundant rows.
Group rows by a column
Why wrong: Grouping is an aggregation, not a cleaning operation.
Replace values
Replacing values (e.g., nulls or errors) cleans data.
Merge queries
Why wrong: Merging combines tables, not a cleaning transform.
Change data type
Changing data types ensures columns are correctly typed for analysis.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Remove duplicates
Option A is correct because the 'Remove duplicates' transform in Power Query identifies and eliminates rows with identical values across selected columns, which is a fundamental data cleaning operation to ensure data integrity and avoid skewed analysis.
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.
Remove duplicates
Why this is correct
Removing duplicates cleans the data by eliminating redundant rows.
Related concept
Read the scenario before looking for a memorised answer.
Group rows by a column
Why it's wrong here
Grouping is an aggregation, not a cleaning operation.
Replace values
Why this is correct
Replacing values (e.g., nulls or errors) cleans data.
Related concept
Read the scenario before looking for a memorised answer.
Merge queries
Why it's wrong here
Merging combines tables, not a cleaning transform.
Change data type
Why this is correct
Changing data types ensures columns are correctly typed for analysis.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse data preparation transforms (like merging or grouping) with data cleaning transforms, leading them to select options that are actually for data shaping or integration rather than direct data quality improvement.
Detailed technical explanation
Under the hood, 'Remove duplicates' uses a hash-based comparison to detect duplicate rows, and Power Query retains only the first occurrence by default. In real-world scenarios, this is critical when dealing with customer records or transaction logs where duplicate entries can inflate metrics like counts or averages. The 'Replace values' transform performs a simple find-and-replace on selected columns, while 'Change data type' converts column types (e.g., text to number) to enforce schema consistency, both essential for cleaning malformed or inconsistent data.
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: Remove duplicates — Option A is correct because the 'Remove duplicates' transform in Power Query identifies and eliminates rows with identical values across selected columns, which is a fundamental data cleaning operation to ensure data integrity and avoid skewed analysis.
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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