The answer is to reduce the number of visuals on the dashboard and apply page-level filters. This is correct because each visual in a DirectQuery model sends a separate query to the Azure Synapse Analytics dedicated SQL pool; by cutting the number of visuals, you directly reduce the total query load, while page-level filters ensure that only the necessary subset of data is retrieved, minimizing the amount of data transferred and processed per query. On the Microsoft Power BI Data Analyst PL-300 exam, this scenario tests your understanding that when you cannot modify the source system, performance tuning must focus on reducing the query burden from the client side—a common trap is thinking you can rewrite the SQL or add indexes, but those require source changes. A useful memory tip is “Fewer visuals, faster panels”—remember that each visual is a separate round trip, so trimming them and filtering early is your only lever when the source is locked.
PL-300 Prepare the data Practice Question
This PL-300 practice question tests your understanding of prepare the data. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 have a Power BI semantic model that uses DirectQuery to an Azure Synapse Analytics dedicated SQL pool. The model is used by a real-time dashboard. Users report that the dashboard is slow. You need to improve query performance without changing the source system. Which action should you take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reduce the number of visuals on the dashboard and apply page-level filters
Option B is correct because reducing the number of visuals and applying page-level filters directly reduces the number of queries sent to the Azure Synapse Analytics dedicated SQL pool via DirectQuery. Since the source system cannot be changed, the only way to improve performance is to minimize the query load from the dashboard. Page-level filters ensure that only relevant data is queried, and fewer visuals mean fewer separate queries, which collectively reduces latency.
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 on the fact table
Why it's wrong here
Aggregations can improve performance but require additional tables and may not be quick to implement.
✓
Reduce the number of visuals on the dashboard and apply page-level filters
Why this is correct
Fewer visuals and filters reduce the number of queries sent to the source.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable dual storage mode for all tables
Why it's wrong here
Dual mode is for mixed storage, but may not help with DirectQuery performance and could cause complexity.
✗
Disable the 'Reduce queries' option in Power BI Desktop
Why it's wrong here
Disabling query reduction would increase the number of queries, worsening performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume performance improvements must come from data modeling changes (like aggregations or storage modes), but the question explicitly forbids changing the source system, so the only viable approach is to reduce the query load from the client side.
Detailed technical explanation
How to think about this question
In DirectQuery mode, each visual on a dashboard generates one or more T-SQL queries against the Azure Synapse Analytics dedicated SQL pool. Page-level filters are pushed down to the source as WHERE clauses, reducing the result set size and query execution time. Reducing the number of visuals directly decreases the total number of concurrent queries, which is critical because Synapse SQL pools have a limited number of concurrent query slots and may throttle under heavy load. This approach aligns with the principle of minimizing query volume when you cannot optimize the source.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Reduce the number of visuals on the dashboard and apply page-level filters — Option B is correct because reducing the number of visuals and applying page-level filters directly reduces the number of queries sent to the Azure Synapse Analytics dedicated SQL pool via DirectQuery. Since the source system cannot be changed, the only way to improve performance is to minimize the query load from the dashboard. Page-level filters ensure that only relevant data is queried, and fewer visuals mean fewer separate queries, which collectively reduces latency.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.