The correct answer is to create an aggregate table in Power BI that pre-aggregates sales by month and product category. This is the best practice because it dramatically reduces the number of rows the engine must scan—from 10 million granular transactions down to a compact, pre-summarized table—allowing the report to answer queries for monthly and category-level totals almost instantly. On the Microsoft Power BI Data Analyst PL-300 exam, this scenario tests your understanding of the aggregation feature, which automatically redirects high-level queries to the aggregate table while preserving the ability to drill down to detail. A common trap is assuming that simply indexing the source database is sufficient, but in Power BI, pre-aggregation within the model is the key to fast slicer and visual performance. Memory tip: think "Aggregate to accelerate"—pre-summarize at the grain of your slicers and visuals to bypass scanning millions of rows.
PL-300 Model the data Practice Question
This PL-300 practice question tests your understanding of model 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.
You are modeling data from an Azure SQL Database into Power BI. The source table 'Sales' contains 10 million rows. You need to ensure that the data model supports fast query performance for a report that shows sales by month and product category. The report uses a slicer for year. What is the best practice for improving performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create an aggregate table in Power BI that pre-aggregates sales by month and product category.
Option D is correct because creating an aggregate table in Power BI that pre-aggregates sales by month and product category drastically reduces the number of rows the report must scan, from 10 million to a much smaller set of aggregated rows. This enables fast query performance for the slicer and visual-level filters, as Power BI can leverage the aggregate table via its aggregation feature, which automatically redirects queries to the pre-summarized data when possible.
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.
✗
Disable the auto-date/time feature.
Why it's wrong here
This reduces model size but doesn't address aggregation needs.
✗
Increase the data load frequency to every 15 minutes.
Use DirectQuery mode to query the source database directly.
Why it's wrong here
DirectQuery may be slower without aggregation.
✓
Create an aggregate table in Power BI that pre-aggregates sales by month and product category.
Why this is correct
Aggregations improve query performance by reducing data volume.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse DirectQuery (option C) as a performance optimization for large data volumes, but in reality, DirectQuery offloads processing to the source and can be slower for aggregated reports, whereas pre-aggregating in Power BI (option D) is the correct approach for fast in-memory query performance.
Detailed technical explanation
How to think about this question
Under the hood, Power BI's aggregation feature creates a mapping between the detailed table and the pre-aggregated table; when a query requests measures at the granularity of month and product category, the engine transparently routes the query to the aggregate table, avoiding scanning the fact table. This is similar to materialized views in databases but leverages Power BI's in-memory columnar storage (VertiPaq) for the aggregate, ensuring sub-second response times even for billions of source rows. In real-world scenarios, you can combine multiple aggregate tables at different granularities (e.g., by year, by quarter) and let Power BI automatically select the most efficient one based on the filter context.
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.
Model the data — This question tests Model the data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create an aggregate table in Power BI that pre-aggregates sales by month and product category. — Option D is correct because creating an aggregate table in Power BI that pre-aggregates sales by month and product category drastically reduces the number of rows the report must scan, from 10 million to a much smaller set of aggregated rows. This enables fast query performance for the slicer and visual-level filters, as Power BI can leverage the aggregate table via its aggregation feature, which automatically redirects queries to the pre-summarized data when possible.
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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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