The correct answer is to filter rows and columns during import using Power Query to reduce data size. This works because Power Query can apply query folding to push data reduction operations upstream, meaning unnecessary columns and rows are stripped away before the data is fully loaded into Power BI Desktop’s memory model. For a 200 MB CSV file, this dramatically cuts the processing overhead and avoids import failures caused by memory exhaustion. On the PL-300 exam, this scenario tests your understanding of data ingestion optimization—a core skill for handling large flat files. A common trap is to assume you should increase system RAM or split the file manually; instead, remember that Power Query’s built-in filtering is the intended solution. Memory tip: “Filter first, model later”—always trim your data at the source to keep imports fast and stable.
PL-300 Prepare the data Practice Question
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 a large CSV file (200 MB) into Power BI Desktop. The import is very slow and sometimes fails. What should you do to improve performance?
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 columns during import using Power Query to reduce data size.
Option C is correct because filtering rows and columns during import using Power Query reduces the data volume loaded into the Power BI Desktop model, directly addressing the root cause of slow imports and failures when handling a 200 MB CSV file. Power Query's query folding capabilities push data reduction operations (e.g., removing unnecessary columns, filtering rows based on conditions) upstream, minimizing memory and processing overhead during the import phase. This approach is a standard best practice for optimizing data ingestion in Power BI Desktop, especially for large flat files.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use Power BI Service to import the file instead.
Why it's wrong here
Power BI Service does not import CSV files directly.
✗
Remove all relationships before import.
Why it's wrong here
Relationships are created after import, not during.
✓
Filter rows and columns during import using Power Query to reduce data size.
Why this is correct
Reducing data volume before loading improves performance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Upgrade to Power BI Premium.
Why it's wrong here
Upgrading does not directly improve CSV import performance in Desktop.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume upgrading to Premium or using the Service will magically fix performance issues, but the PL-300 exam emphasizes that data reduction during import (via Power Query filtering) is the primary technique to optimize large file ingestion in Power BI Desktop.
Detailed technical explanation
How to think about this question
Under the hood, Power Query uses the M language to transform data, and when importing a CSV, it reads the entire file into memory unless filters are applied early via the 'Navigation' step or 'Table.SelectRows'/'Table.SelectColumns' functions. In real-world scenarios, a 200 MB CSV may contain millions of rows with many irrelevant columns; filtering at the source reduces the dataset to a manageable size (e.g., 50 MB), preventing out-of-memory errors and drastically reducing import time. A subtle behavior is that Power Query's 'Remove Rows' or 'Keep Rows' operations do not trigger query folding for CSV files (since they lack a database query engine), but they still reduce the data held in memory during the load phase.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Prepare the data — This question tests Prepare the data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Filter rows and columns during import using Power Query to reduce data size. — Option C is correct because filtering rows and columns during import using Power Query reduces the data volume loaded into the Power BI Desktop model, directly addressing the root cause of slow imports and failures when handling a 200 MB CSV file. Power Query's query folding capabilities push data reduction operations (e.g., removing unnecessary columns, filtering rows based on conditions) upstream, minimizing memory and processing overhead during the import phase. This approach is a standard best practice for optimizing data ingestion in Power BI Desktop, especially for large flat files.
What should I do if I get this PL-300 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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