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 loading data from a SQL Server database into Power BI. You notice that the import takes a long time because the source table contains many rows. You only need a subset of rows based on a date filter. What should you do to improve performance?
Remove unnecessary columns in Power Query.
Why wrong: Reduces columns but not rows.
Use a SQL query with a WHERE clause in the Power Query Editor.
Query folding pushes filter to source.
Load all data and then apply a filter in Power BI.
Why wrong: Loads unnecessary data.
Enable incremental refresh on the dataset.
Why wrong: Incremental refresh is for scheduled refresh, not initial load.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use a SQL query with a WHERE clause in the Power Query Editor.
Option B is correct because using a SQL query with a WHERE clause in Power Query Editor pushes the date filter down to the SQL Server database, reducing the amount of data transferred over the network and imported into Power BI. This query folding technique leverages the database engine's indexing and processing power, which is far more efficient than filtering after import. By retrieving only the necessary rows upfront, you minimize both network latency and memory consumption in Power BI.
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 unnecessary columns in Power Query.
Why it's wrong here
Reduces columns but not rows.
Use a SQL query with a WHERE clause in the Power Query Editor.
Why this is correct
Query folding pushes filter to source.
Related concept
Read the scenario before looking for a memorised answer.
Load all data and then apply a filter in Power BI.
Why it's wrong here
Loads unnecessary data.
Enable incremental refresh on the dataset.
Why it's wrong here
Incremental refresh is for scheduled refresh, not initial load.
Common exam traps
The trap here is that candidates often choose 'Remove unnecessary columns' (Option A) thinking it reduces data volume, but they overlook that row count reduction via query folding has a far greater impact on import performance than column reduction.
Detailed technical explanation
Query folding in Power Query translates M expressions into native SQL queries executed on the source database. When you use a SQL query with a WHERE clause, Power Query sends that exact query to SQL Server, which uses its query optimizer and indexes to efficiently scan only the relevant rows. This contrasts with post-import filtering, where all rows must be serialized, transmitted via TDS protocol, and deserialized into Power BI's in-memory columnar engine (VertiPaq), causing unnecessary I/O and CPU overhead.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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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: Use a SQL query with a WHERE clause in the Power Query Editor. — Option B is correct because using a SQL query with a WHERE clause in Power Query Editor pushes the date filter down to the SQL Server database, reducing the amount of data transferred over the network and imported into Power BI. This query folding technique leverages the database engine's indexing and processing power, which is far more efficient than filtering after import. By retrieving only the necessary rows upfront, you minimize both network latency and memory consumption in Power BI.
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
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