DP-700 Monitor and Optimize an Analytics Solution Practice Question
You are managing a Microsoft Fabric Capacity. You need to identify which two metrics are most effective for tracking the 'smoothing' behavior of your capacity during peak usage. Which two metrics should you monitor?
⚠ Common exam trap
Candidates often select general storage metrics or user login counts, missing the specific metrics that directly measure capacity smoothing and request rejection.
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
✓
Capacity Utilization percentage
Smoothing is a key Fabric feature that distributes short-term spikes in demand over a five-minute window to avoid throttling. Monitoring 'Capacity Utilization' and 'Throttling Events' provides a complete picture of whether the smoothing mechanism is successfully absorbing spikes or if the workload is consistently exceeding the assigned SKU capacity. These metrics are vital for capacity planning and ensuring that users do not experience service interruptions when multiple pipelines run concurrently or when interactive queries surge.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Capacity Utilization percentage
Why this is correct
Capacity utilization represents the actual compute usage against your SKU. Tracking this metric allows you to visualize how smoothing impacts the overall load, showing you if your workloads are consistently peaking or if they remain within the sustained performance thresholds set by your current capacity tier.
- ✓
Throttling Events
Why this is correct
Throttling events occur when the smoothing mechanism cannot fully compensate for sustained demand exceeding the capacity limits. Monitoring these events is essential to determine if your current SKU is under-provisioned, as it directly indicates where smoothing has failed to prevent performance degradation for the users involved.
- ✗
Data storage growth rate
Why it's wrong here
Data storage growth rate measures the volume of data added to OneLake over time. While important for cost management and storage planning, it has no direct relationship to the compute smoothing mechanism, which is exclusively concerned with CPU and memory utilization during active query and pipeline execution.
- ✗
Network latency between regions
Why it's wrong here
Network latency between regions is an infrastructure metric that is generally outside the control of capacity management. It does not provide actionable insights into how Fabric's compute smoothing handles bursty workloads, making it irrelevant for monitoring the effectiveness of internal capacity resource allocation and load balancing.
- ✗
Workspace file count
Why it's wrong here
Workspace file count measures the number of objects stored in the environment. This metric is useful for auditing and storage optimization, but it does not track compute performance or the smoothing logic applied to Spark jobs or SQL queries executed within the Fabric capacity environment.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
This DP-700 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 DP-700 exam.