The answer is to push the Year filter into the SQL query using a native query. This improves Power Query performance because it forces query folding to occur at the database level, meaning the SQL server applies the filter before sending data to Power BI, drastically reducing the volume of rows transferred. By pushing filters to SQL, you minimize memory consumption and processing time in Power Query, which is the most efficient approach for large datasets. On the PL-300 exam, this tests your understanding of query folding and data source optimization—a common trap is assuming all Power Query transformations are equally efficient, when in fact only those that fold back to the source are performant. Remember, the closer the filter is to the source, the faster the query: think “filter first, fold fast.”
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.
Exhibit
let
Source = Sql.Database("server", "database"),
Sales = Source{[Schema="dbo"][Item="Sales"]}[Data],
FilteredRows = Table.SelectRows(Sales, each [Year] >= 2020),
GroupedRows = Table.Group(FilteredRows, {"Region"}, {{"TotalSales", each List.Sum([Amount]), type number}})
in
GroupedRows
Refer to the exhibit. The Power Query M code connects to a SQL database and groups sales data. You notice the query is slow. Which change would most likely improve performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Push the Year filter into the SQL query using a native query
Option B is correct because pushing the Year filter into the SQL query via a native query reduces the amount of data transferred from SQL Server to Power Query. By filtering at the source, Power Query only loads the relevant rows, which minimizes memory usage and processing time. This leverages query folding, which is the most efficient way to handle large datasets in Power Query.
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 GroupedRows step to simplify
Why it's wrong here
Grouping is necessary for the requirement.
✓
Push the Year filter into the SQL query using a native query
Why this is correct
Filtering at source reduces data transferred.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove the filter on Year to process all data
Why it's wrong here
Removing filter increases data volume.
✗
Perform grouping in SQL instead of Power Query
Why it's wrong here
Grouping at source can also improve performance, but filtering first is more impactful.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume removing steps or performing grouping in Power Query is always faster, but they overlook the critical concept of query folding and the performance gain from pushing filters to the source database.
Detailed technical explanation
How to think about this question
Query folding in Power Query allows transformations like filters and groupings to be translated into SQL statements that execute on the database server, reducing data movement. When a filter is applied in Power Query without folding (e.g., using a native query or ensuring the step is foldable), the entire table is loaded into Power Query's memory, which can cause performance degradation with large tables. In real-world scenarios, combining filters and aggregations in a single SQL query can further optimize performance by leveraging database indexes and parallel execution.
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: Push the Year filter into the SQL query using a native query — Option B is correct because pushing the Year filter into the SQL query via a native query reduces the amount of data transferred from SQL Server to Power Query. By filtering at the source, Power Query only loads the relevant rows, which minimizes memory usage and processing time. This leverages query folding, which is the most efficient way to handle large datasets in Power Query.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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