The answer is to remove the unnecessary columns in Power Query before loading. This is the most efficient approach because Power Query performs data transformation upstream of the Power BI model, meaning that columns you delete at this stage never enter the data engine, reducing memory consumption and improving refresh performance from the start. On the PL-300 exam, this concept tests your understanding of query folding and the principle of filtering data as early as possible in the ETL process—a common trap is selecting a post-load method like hiding columns in the report view, which still consumes resources. When you need to reduce columns imported from a CSV in Power Query, always apply the removal in the Query Editor rather than after the data is loaded. Memory tip: “Trim before you import, not after you sort”—cut columns in the query stage to keep your model lean.
PL-300 Prepare the data Practice Question
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 importing a large dataset from a CSV file using Power Query. The file contains 50 columns, but you only need 10 for your report. What is the most efficient way to reduce the amount of data loaded into the model?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove the unnecessary columns in Power Query before loading.
Option A is correct because Power Query processes data before it enters the Power BI model. Removing unnecessary columns at the query stage reduces the amount of data loaded into memory, improving performance and reducing storage. This is the most efficient approach as it minimizes the dataset size from the start, unlike post-load methods that still consume resources.
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.
✓
Remove the unnecessary columns in Power Query before loading.
Why this is correct
Removing columns early reduces the data volume.
Related concept
Read the scenario before looking for a memorised answer.
✗
Load all columns and then hide the unnecessary ones in the report.
Why it's wrong here
Hidden columns are still loaded into the model.
✗
Use a SQL query to select only the needed columns if the data source supports it.
Why it's wrong here
CSV does not support SQL queries.
✗
Create a DAX calculated table that selects only the needed columns.
Why it's wrong here
Calculated tables still require loading all columns initially.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'hiding' columns with actually removing them, or incorrectly assume SQL-like filtering can be applied to flat files, leading them to choose options that still load unnecessary data into memory.
Detailed technical explanation
How to think about this question
Power Query uses the M language to transform data during the 'Get Data' phase, before it is compressed and stored in the VertiPaq engine. Removing columns early reduces the column cardinality and the size of the column dictionaries, directly impacting compression ratios and query performance. In real-world scenarios, this can reduce model size by over 50% when only a fraction of columns are needed.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Remove the unnecessary columns in Power Query before loading. — Option A is correct because Power Query processes data before it enters the Power BI model. Removing unnecessary columns at the query stage reduces the amount of data loaded into memory, improving performance and reducing storage. This is the most efficient approach as it minimizes the dataset size from the start, unlike post-load methods that still consume resources.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You have a Power BI report that uses a dataset with many columns. You want to reduce the dataset size by removing columns that are not used in any report visual. What is the best practice?
medium
✓ A.Remove the columns in Power Query Editor before loading
B.Use the Q&A feature to exclude columns
C.Apply report-level filters to exclude the columns
D.Hide the columns in the model view
Why A: Option A is correct because removing columns in Power Query Editor before they are loaded into the data model physically excludes them from the dataset, reducing its size and improving performance. This is the only method that prevents unused columns from consuming memory and storage in the in-memory VertiPaq engine.
Last reviewed: Jun 24, 2026
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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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