PL-300 Manage and secure Power BI Practice Question
You have a Power BI capacity that is frequently hitting its memory limits, causing refreshes to fail. You need to reduce memory usage without increasing capacity size. What should you do?
Answer choices
Why each option matters
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 parallel data refresh operations.
The correct answer is B: reducing the number of parallel data refresh operations lowers peak memory usage because each concurrent refresh consumes memory for its own data processing, and running fewer at once means fewer simultaneous memory spikes on the capacity. This directly addresses the memory-limit failures without requiring a larger capacity SKU. Option A is wrong because increasing eviction time keeps unused data in memory longer, which would increase rather than reduce memory pressure. Option C is wrong because RLS is not the primary driver of refresh memory usage, and removing it would not reliably solve capacity memory limits. Option D is wrong because more frequent refreshes increase overall memory and CPU load, making the problem worse.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the eviction time for unused data.
Why it's wrong here
Increasing the eviction time would instruct the Power BI memory manager to keep unused data pages in memory longer. While this can improve query performance for cached data, it directly increases the capacity's memory footprint and heightens memory pressure, which is the opposite of what is needed when the capacity is frequently hitting its limits. It does nothing to reduce peak memory consumption from refreshes or queries.
- ✓
Reduce the number of parallel data refresh operations.
Why this is correct
Reducing the number of parallel data refresh operations lowers the peak memory and CPU demand on the capacity because each refresh must load and compress the entire dataset into memory. By serializing refreshes, you flatten the resource usage curve, preventing the capacity from reaching its memory threshold during overlapping refresh jobs. This is a direct, effective lever to avoid capacity throttling and eviction.
- ✗
Remove row-level security (RLS) from the dataset.
Why it's wrong here
Removing row-level security does not materially reduce memory consumption because RLS is applied as a query-time filter in the Analysis Services engine, not by duplicating data for each role. The dataset remains a single compressed copy in memory regardless of RLS definitions, so the capacity limit is not caused by RLS overhead. In fact, the dynamic DAX filters used by RLS are compiled and cached separately, but their metadata footprint is negligible compared to data and refresh memory.
- ✗
Increase the frequency of scheduled refreshes.
Why it's wrong here
Increasing the frequency of scheduled refreshes would add more refresh jobs to the same time window, causing the dataset to be loaded into memory and processed more often. Each refresh spikes memory usage, and overlapping refreshes between datasets (or even the same dataset) will raise aggregate consumption, making capacity limit hits more likely. It also increases CPU usage from compression operations, so it would worsen rather than resolve the problem.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.