DP-700 Monitor and Optimize an Analytics Solution Practice Question
You need to minimize the performance impact of concurrent data refreshes on your Fabric capacity. What is the best strategy?
⚠ Common exam trap
Examinees often assume that adding capacity units or rewriting individual queries is the fastest way to resolve concurrent refresh contention.
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
✓
Stagger scheduled refreshes to distribute load
Staggering schedules is a fundamental optimization technique for managing compute capacity. By spreading out job executions, you prevent multiple resource-intensive processes from competing for the same slots simultaneously, which avoids throttling and ensures consistent performance. This approach is highly effective for maintaining service availability without needing to upgrade to a more expensive, higher-throughput capacity SKU.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Run all pipelines simultaneously at 00:00
Why it's wrong here
Running all pipelines at the same time creates an immediate, massive spike in compute demand. This causes resource contention, potential throttling, and significantly slower execution times for all jobs involved, as they compete for the same limited pool of capacity resources simultaneously.
- ✗
Increase the refresh frequency for all items
Why it's wrong here
Increasing refresh frequency places a higher continuous load on the capacity. This leads to more frequent resource consumption and increases the likelihood of hitting capacity limits, ultimately degrading the overall performance of the workspace and increasing the risk of service throttling.
- ✓
Stagger scheduled refreshes to distribute load
Why this is correct
Staggering schedules flattens the load profile on your capacity. By ensuring that intensive data refreshes do not overlap, you maximize throughput and minimize the chance of hitting compute limits, ensuring that all jobs have enough resources to finish within their windows.
- ✗
Limit access to the workspace for users
Why it's wrong here
Restricting user access does not address the underlying compute demand generated by automated data refreshes. Even if users are blocked, the scheduled jobs will still run and consume the same amount of capacity, providing no relief to the performance bottleneck.
About these practice questions
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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
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