Quick Answer
The answer is to perform aggregation in SQL before importing to Power BI. This is correct because pushing the aggregation logic to the SQL Server database engine reduces the data volume from millions of raw transaction rows to a much smaller, pre-summarized result set, dramatically lowering memory consumption and speeding up report rendering in Power BI. On the Microsoft Power BI Data Analyst PL-300 exam, this scenario tests your understanding of query folding and data reduction techniques—a common trap is to assume Power Query can handle all transformations efficiently, but for large datasets, early filtering and aggregation at the source is the performance-optimized approach. Remember the memory tip: "Aggregate at the source, not in the course"—meaning do the heavy lifting in SQL before the data ever reaches Power BI’s model.
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.
A company uses Power BI to analyze sales data from a SQL Server database. The database contains a table 'Sales' with 10 million rows. The business analysts need to create daily reports that aggregate sales by region and product category. To optimize report performance, which data preparation technique should be applied?
Increase the row limit in Power Query to load all rows.
Why wrong: Loading all rows increases data volume.
Remove unused columns from the query.
Why wrong: Removing unused columns reduces width but not row count.
Import the entire table and aggregate in Power BI.
Why wrong: Importing all rows is inefficient.
Perform aggregation in SQL before importing.
Aggregating at source reduces rows significantly.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Perform aggregation in SQL before importing.
Option D is correct because performing aggregation in SQL before importing reduces the data volume from 10 million rows to a much smaller aggregated result set. This minimizes memory consumption and speeds up report rendering in Power BI, as the heavy lifting is done on the SQL Server engine rather than in Power Query or the Power BI data model.
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.
Increase the row limit in Power Query to load all rows.
Why it's wrong here
Loading all rows increases data volume.
Remove unused columns from the query.
Why it's wrong here
Removing unused columns reduces width but not row count.
Import the entire table and aggregate in Power BI.
Why it's wrong here
Importing all rows is inefficient.
Perform aggregation in SQL before importing.
Why this is correct
Aggregating at source reduces rows significantly.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often assume removing columns or filtering rows is sufficient, but the question specifically targets aggregation of millions of rows, where source-side aggregation is the only scalable solution.
Detailed technical explanation
Under the hood, Power Query can push query folding to SQL Server when using native SQL queries or certain M functions, allowing the database to perform aggregations like GROUP BY before data is transferred. In real-world scenarios, this technique is critical for large fact tables (e.g., 100M+ rows) where importing raw data would exceed Power BI Premium per-node memory limits or cause timeouts during scheduled refresh.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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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: Perform aggregation in SQL before importing. — Option D is correct because performing aggregation in SQL before importing reduces the data volume from 10 million rows to a much smaller aggregated result set. This minimizes memory consumption and speeds up report rendering in Power BI, as the heavy lifting is done on the SQL Server engine rather than in Power Query or the Power BI data model.
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 11, 2026
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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