Quick Answer
The answer is to filter rows and remove unnecessary columns in Power Query, as this directly reduces the data volume before it ever reaches the Power BI data model. By applying these transformations early in the import process, you minimize the amount of data Power Query must process and store, which is the most effective way to reduce CSV import size and speed up loading. On the Microsoft Power BI Data Analyst PL-300 exam, this scenario tests your understanding of query folding and data reduction principles—a common trap is thinking that changing the data type or using aggregations later in the model will solve performance issues, but those steps still require loading the full dataset first. The key insight is that Power Query’s “Query Settings” pane allows you to apply row filters and column removals at the source, which is far more efficient than post-load transformations. Memory tip: think “cut before you carry”—trim the data at the gate, not after it’s inside.
This PL-300 practice question tests your understanding of prepare the data. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 importing data from a large CSV file (5 GB) into Power BI. The import takes too long and you need to reduce the data volume. What is the most effective approach in Power Query?
Disable loading of unrelated queries.
Why wrong: Does not affect the CSV import.
Increase query parallelism in options.
Why wrong: Does not reduce data volume.
Filter rows and remove unnecessary columns in Power Query.
Directly reduces the amount of data loaded.
Enable 'Query Reduction' settings.
Why wrong: Optimizes query folding but not data size.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Filter rows and remove unnecessary columns in Power Query.
Option C is correct because filtering rows and removing unnecessary columns in Power Query directly reduces the amount of data loaded into Power BI's data model. This is the most effective way to reduce data volume from a large CSV file, as it minimizes the data that Power Query must process and store, directly addressing the import time issue.
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.
Disable loading of unrelated queries.
Why it's wrong here
Does not affect the CSV import.
Increase query parallelism in options.
Why it's wrong here
Does not reduce data volume.
Filter rows and remove unnecessary columns in Power Query.
Why this is correct
Directly reduces the amount of data loaded.
Related concept
Read the scenario before looking for a memorised answer.
Enable 'Query Reduction' settings.
Why it's wrong here
Optimizes query folding but not data size.
Common exam traps
The trap here is that candidates often confuse 'Query Reduction' settings (which affect report interaction queries) with data volume reduction during import, leading them to incorrectly select Option D.
Detailed technical explanation
Under the hood, Power Query evaluates transformations lazily, meaning it only materializes data when necessary. By filtering rows and removing columns early in the query, you reduce the number of rows and columns that Power Query must read from the CSV file, which directly reduces memory usage and processing time. In a real-world scenario, a 5 GB CSV file might contain many irrelevant columns (e.g., audit fields) or historical rows that can be filtered out, making this approach highly effective for large datasets.
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: Filter rows and remove unnecessary columns in Power Query. — Option C is correct because filtering rows and removing unnecessary columns in Power Query directly reduces the amount of data loaded into Power BI's data model. This is the most effective way to reduce data volume from a large CSV file, as it minimizes the data that Power Query must process and store, directly addressing the import time issue.
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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