DP-700 Monitor and Optimize an Analytics Solution Practice Question
You are managing a large-scale data ingestion pipeline that runs every hour. Recently, the pipeline has started to fail with 'Concurrency Limit Exceeded' errors. You have several other pipelines running in the same workspace. How should you optimize the environment to resolve this error?
⚠ Common exam trap
Candidates often suggest scaling up the Fabric SKU immediately. While this increases concurrency limits, it is a costly solution for an issue that can often be resolved by optimizing pipeline scheduling.
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
✓
Implement a staggered schedule for the pipelines in the workspace.
Fabric capacities have limits on the number of concurrent operations that can run. When multiple pipelines or activities start simultaneously, they can exceed these limits. Implementing a more staggered schedule or using pipeline concurrency settings ensures that the workload stays within the allowed limits of the capacity SKU without failing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the 'Invoke Pipeline' activity with 'Wait on completion' disabled.
Why it's wrong here
Disabling 'Wait on completion' allows the parent pipeline to continue without waiting for the child, which actually increases the number of concurrent processes running at once. This would likely exacerbate the concurrency limit issue rather than resolving it.
- ✗
Increase the 'Max concurrent runs' setting in the pipeline properties.
Why it's wrong here
The 'Max concurrent runs' setting limits how many instances of the same pipeline can run at once. Increasing this might allow more instances to start, but it does not bypass the underlying Fabric capacity's total concurrency limits, potentially leading to more frequent system-level throttling.
- ✓
Implement a staggered schedule for the pipelines in the workspace.
Why this is correct
By staggering the start times of different pipelines, you spread the resource demand over a longer period. This prevents a massive spike in concurrent requests at the top of the hour, allowing the capacity to handle each request within its defined concurrency and Compute Unit limits.
- ✗
Switch all pipelines to use the 'Small' Spark pool size.
Why it's wrong here
While using smaller Spark pools might reduce the memory used per job, it does not change the number of concurrent activities being triggered by the Data Factory engine. Concurrency limits often apply to the orchestration layer and the total number of active tasks across the capacity.
Visual reference
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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.