The answer is to create aggregations in Power BI on the large table. This is correct because aggregations allow the DirectQuery model to pre-summarize data at the source or within Power BI, so when users request aggregated data, the engine queries a much smaller, pre-calculated table instead of scanning millions of rows in Azure SQL. This dramatically reduces query latency and is a core technique for improving DirectQuery performance for large tables. On the PL-300 exam, this tests your understanding of hybrid table design and performance tuning for DirectQuery models—a common trap is thinking that indexing the source database alone is sufficient, but the exam emphasizes that Power BI aggregations work with the source to offload heavy summarization. Remember the memory tip: “Aggregate first, query last”—always build aggregations on large tables before expecting fast aggregated results.
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.
You are connecting to an Azure SQL database using DirectQuery. The database has a large table with millions of rows. Users need to see aggregated data quickly. What should you implement to improve query performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create aggregations in Power BI on the large table.
Option A is correct because creating aggregations in Power BI on the large table allows the DirectQuery model to pre-aggregate data at the source or in Power BI, reducing the volume of data queried and improving response times for aggregated results. This is a key performance optimization for DirectQuery models with large tables, as it avoids scanning millions of rows for every 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.
✓
Create aggregations in Power BI on the large table.
Why this is correct
Aggregations reduce the amount of data queried from the source.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the memory limit of the Power BI Desktop.
Why it's wrong here
Memory limit does not affect DirectQuery performance.
✗
Use a composite model with a smaller imported table.
Why it's wrong here
Composite models are for combining import and DirectQuery, not a direct performance fix.
✗
Add indexes to the database table.
Why it's wrong here
Indexing is not controlled from Power BI.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse database-side optimizations (like indexes) with Power BI-side optimizations (like aggregations), leading them to choose Option D, but the question explicitly asks what you should implement in Power BI, not in the database.
Detailed technical explanation
How to think about this question
Aggregations in Power BI can be defined at the table level and stored either in the Power BI model (cached) or pushed down to the source database. When using DirectQuery, aggregations are typically cached in Power BI, allowing the engine to serve aggregated queries from the cache instead of hitting the large table, which drastically reduces query latency. In real-world scenarios, this is critical for dashboards with high concurrency, as it prevents the database from being overwhelmed by repeated aggregation queries.
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: Create aggregations in Power BI on the large table. — Option A is correct because creating aggregations in Power BI on the large table allows the DirectQuery model to pre-aggregate data at the source or in Power BI, reducing the volume of data queried and improving response times for aggregated results. This is a key performance optimization for DirectQuery models with large tables, as it avoids scanning millions of rows for every 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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You have a Power BI dataset that uses DirectQuery to an Azure SQL Database. Users complain that reports take too long to load. You suspect that the database is overwhelmed by queries. What should you do to improve performance while keeping DirectQuery?
hard
✓ A.Reduce the number of visuals per page and apply slicers to limit data.
B.Increase the Power BI Premium capacity size.
C.Create aggregations in the dataset.
D.Change the storage mode to Import.
Why A: Option A is correct because reducing the number of visuals per page and applying slicers to limit data reduces the number of DAX queries sent to the Azure SQL Database via DirectQuery. Each visual generates at least one query, so fewer visuals mean fewer concurrent queries, and slicers add WHERE clauses that reduce the result set size, lowering the load on the database.
Variation 2. You have a Power BI dataset that uses DirectQuery to an Azure Synapse Analytics dedicated SQL pool. You need to improve query performance. Which THREE actions should you take?
hard
✓ A.Create indexes on columns used in filters
B.Normalize the data warehouse tables
✓ C.Create aggregated tables in the data source
✓ D.Use materialized views
E.Switch the dataset to Import mode
Why A: Creating indexes on columns used in filters is correct because DirectQuery translates Power BI filter operations into SQL queries against the Azure Synapse Analytics dedicated SQL pool. Indexes on those columns accelerate row-level filtering by reducing the number of pages scanned, directly improving query response times.
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Question Discussion
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