The correct answer is to apply row-level filters in Power Query to import only relevant rows. This is the most effective way to filter rows before loading in Power Query to reduce data, as it directly minimizes the volume of data brought into the Power BI model. By using Power Query’s built-in filtering capabilities—such as row selection, conditional columns, or SQL-based query folding—you ensure that only rows meeting specific criteria are loaded, which dramatically shrinks the in-memory footprint and accelerates both query and refresh performance, especially for massive fact tables in Azure SQL Database. On the Microsoft Power BI Data Analyst PL-300 exam, this concept tests your understanding of data reduction strategies at the data source layer, a critical skill for optimizing large-scale models. A common trap is assuming that filtering in DAX or using report-level slicers is sufficient, but those methods still load all rows into memory. Remember the memory tip: “Filter at the source, not in the report”—if you can push filtering back to Power Query, you save memory before the model even loads.
PL-300 Prepare the data Practice Question
This PL-300 practice question tests your understanding of prepare the data. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
Your organization uses Power BI to analyze sales data stored in Azure SQL Database. The data model includes a fact table with millions of rows. To improve performance, you need to reduce the amount of data loaded into the model. Which action should you take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply row-level filters in Power Query to import only relevant rows
Option A is correct because applying row-level filters in Power Query reduces the volume of data imported into the Power BI model by only loading rows that meet specific criteria. This directly minimizes the data footprint in memory, improving query and refresh performance, especially for fact tables with millions of rows stored in Azure SQL Database.
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.
✓
Apply row-level filters in Power Query to import only relevant rows
Why this is correct
Filtering rows in Power Query reduces the data imported into the model, improving performance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use calculated tables in DAX to summarize data
Why it's wrong here
Calculated tables are created after data is loaded, they do not reduce the initial load.
✗
Disable the Auto Date/Time feature
Why it's wrong here
Disabling auto date/time reduces model size but does not reduce the amount of data loaded from the source.
✗
Configure incremental refresh with a date filter
Why it's wrong here
Incremental refresh schedules refreshes but does not reduce the initial load unless combined with filter.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse incremental refresh with reducing the initial data load, but incremental refresh only optimizes refresh cycles over time and does not limit the first full load unless combined with a date filter in Power Query.
Detailed technical explanation
How to think about this question
Under the hood, Power Query uses M language to push query folding operations like WHERE clauses directly to Azure SQL Database, leveraging the database engine's indexing and query optimization to filter rows before data transfer. This reduces network I/O and memory pressure in the VertiPaq engine, which stores compressed columnar data. In real-world scenarios, filtering on a high-cardinality column like OrderDate can reduce a 50-million-row table to a few hundred thousand rows, drastically improving report responsiveness.
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: Apply row-level filters in Power Query to import only relevant rows — Option A is correct because applying row-level filters in Power Query reduces the volume of data imported into the Power BI model by only loading rows that meet specific criteria. This directly minimizes the data footprint in memory, improving query and refresh performance, especially for fact tables with millions of rows stored in Azure SQL Database.
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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