PL-300 Manage and secure Power BI Practice Question
You are a Power BI administrator. The company has a premium capacity that many users publish reports to. Recently, users have reported that some reports are slow to load. You suspect that the capacity is being overused by certain large datasets. You need to identify which workspaces and datasets are consuming the most memory and CPU resources on the capacity. You want to use a tool that provides historical metrics and can be queried. 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
✓
Install the Power BI Premium Capacity Metrics app from AppSource.
The Power BI Premium Capacity Metrics app (option A) is the correct tool because it is installed from AppSource and provides historical, queryable metrics on memory and CPU consumption per workspace and dataset on a Premium capacity, directly addressing the need to find the top resource consumers. It surfaces dataset sizes, refresh durations, CPU usage, and memory usage over time, which is exactly what the administrator requires. Option B, Performance Analyzer, only measures rendering and query performance of individual reports in Power BI Desktop and does not give capacity-wide historical resource metrics. Option C, the admin portal, shows current capacity health and utilization but not the detailed historical, queryable per-dataset metrics needed. Option D, activity logs in Log Analytics, capture user and admin activity events, not the granular memory and CPU consumption metrics of datasets on the capacity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Install the Power BI Premium Capacity Metrics app from AppSource.
Why this is correct
The Premium Capacity Metrics app from AppSource is Microsoft's official monitoring solution for Premium capacities. It periodically pulls telemetry from the capacity metrics API, stores it in a dedicated dataset, and presents historical CPU, memory, query counts, and refresh statistics per workspace and dataset. The underlying data is DAX-queryable, enabling a three-month retrospective analysis, which makes it the only listed tool that satisfies the stated need for historical resource-level metrics.
- ✗
Use Performance Analyzer in Power BI Desktop for each report.
Why it's wrong here
Performance Analyzer in Power BI Desktop is a client-side diagnostic tool that measures durations of DAX queries, visuals, and page loads for a single report in an authoring session. It does not collect server-side CPU or memory consumption from the Premium capacity, and it cannot aggregate or retain historical data for trend analysis. Because it is session-bound and per-report, it is irrelevant for investigating three months of capacity-level memory and CPU usage across multiple workspaces and datasets.
- ✗
Use the Power BI admin portal to view capacity metrics in real-time.
Why it's wrong here
The Power BI admin portal's capacity health page provides a real-time snapshot of CPU and memory utilization for a Premium capacity, but this telemetry is not stored as a historical series. Without a built-in retention or querying mechanism, you cannot retrospectively build a three-month trend or correlate past resource spikes with specific datasets and workspaces. It is designed for immediate troubleshooting, not as a queryable dataset for capacity planning, so it does not meet the requirement for historical, queryable metrics.
- ✗
Enable Power BI activity logs and query Log Analytics for capacity metrics.
Why it's wrong here
Power BI activity logs capture user-level audit events such as report views, edits, exports, and permission changes; they do not contain CPU or memory counters from the Premium capacity. Even if you enable diagnostic settings to send these logs to Log Analytics, you are only making those audit events searchable, not the capacity resource telemetry. To get memory and CPU per dataset and workspace, you need the capacity metrics API or the app built on it, so this approach does not yield the required metrics.
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Written by Johnson Ajibi, MSc IT Security
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