This PL-300 practice question tests your understanding of model 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 has a Power BI semantic model that uses DirectQuery to a SQL Server database. The model includes a large fact table with 100 million rows. Users are experiencing slow report performance. Which THREE actions should the developer take 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
✓
Configure incremental refresh to limit data retrieved per query.
Option A is correct because incremental refresh limits the amount of data retrieved per query by partitioning the fact table into smaller, manageable date ranges. In a DirectQuery model, this reduces the volume of data scanned by SQL Server for each query, directly improving report performance by minimizing the data transfer and query execution time.
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.
✓
Configure incremental refresh to limit data retrieved per query.
Why this is correct
Incremental refresh reduces the amount of data queried at a time.
Related concept
Read the scenario before looking for a memorised answer.
✓
Create indexes on columns used in filters and relationships.
Why this is correct
Indexes speed up query execution at the database level.
Related concept
Read the scenario before looking for a memorised answer.
✓
Remove unused columns from the fact table.
Why this is correct
Fewer columns mean less data retrieved from the source.
Related concept
Read the scenario before looking for a memorised answer.
✗
Hide columns that are not needed in reports.
Why it's wrong here
Hiding columns only affects user visibility, not query performance.
✗
Add calculated columns to precompute aggregations.
Why it's wrong here
In DirectQuery, calculated columns are computed by the source, increasing query complexity.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse hiding columns (a cosmetic change) with removing columns (a structural optimization), and they mistakenly believe calculated columns can improve query performance when in fact they add overhead in DirectQuery models.
Detailed technical explanation
How to think about this question
In DirectQuery mode, Power BI translates report visuals into SQL queries sent to the source database. Incremental refresh works by creating date-range partitions that are refreshed independently, but in DirectQuery it is used to filter the query to only the most recent or relevant partitions, reducing the scan scope. Indexes on filter and relationship columns (Option B) accelerate SQL Server's query execution by enabling index seeks instead of full table scans, which is critical for a 100-million-row fact table. Removing unused columns (Option C) reduces the data transferred over the network and the memory footprint of the query result, as Power BI only requests the columns needed for the visual.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: Configure incremental refresh to limit data retrieved per query. — Option A is correct because incremental refresh limits the amount of data retrieved per query by partitioning the fact table into smaller, manageable date ranges. In a DirectQuery model, this reduces the volume of data scanned by SQL Server for each query, directly improving report performance by minimizing the data transfer and query execution time.
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.
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Question Discussion
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