DP-700 Monitor and Optimize an Analytics Solution Practice Question
You need to quickly identify all failed Data Factory pipeline runs and Spark jobs across multiple workspaces in your Microsoft Fabric environment. Which TWO actions should you perform in the Fabric Monitoring Hub?
⚠ Common exam trap
Candidates often attempt to check each individual workspace manually or use the 'Recent' list. They fail to realize that the Monitoring Hub allows filtering by item type across the entire tenant.
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
✓
Apply a filter for the 'Failed' status.
The Fabric Monitoring Hub acts as a centralized dashboard for tracking the health of all activities. It allows users to filter by status and item type across the entire environment. This centralized view is essential for data engineers who need to manage multiple pipelines and notebooks without navigating to each individual workspace to check logs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Apply a filter for the 'Failed' status.
Why this is correct
Filtering by status is the most efficient way to isolate jobs that require immediate attention. In a busy environment, this reduces noise and allows engineers to focus on troubleshooting errors. The Monitoring Hub allows this filter to be applied globally across different types of Fabric items simultaneously.
- ✓
Use the 'Item type' filter to select Pipeline and Spark job.
Why this is correct
Since the Monitoring Hub displays all activities including Dataflows and KQL queries, selecting specific item types helps narrow down the results to the relevant engines. This ensures that the view is limited to the specific workloads you are responsible for monitoring and optimizing at that moment.
- ✗
Export the logs to an Azure Log Analytics workspace.
Why it's wrong here
While exporting logs is possible for long-term retention and complex querying, it is not a direct action within the Monitoring Hub to 'identify' jobs quickly. The Hub itself provides the real-time interface needed for this task without the overhead of setting up and querying external logging services.
- ✗
Schedule a daily refresh for the Monitoring Hub dashboard.
Why it's wrong here
The Monitoring Hub is a live system view that updates automatically as jobs progress; it is not a Power BI report that requires a scheduled refresh. Attempting to schedule a refresh is unnecessary and does not help in identifying current failures across the various workspaces in the environment.
- ✗
Configure an alert in the Capacity Metrics app.
Why it's wrong here
The Capacity Metrics app is focused on compute consumption and throttling rather than individual job success or failure. While you can alert on capacity limits, it will not provide a list of failed Spark jobs or Pipelines, making it the wrong tool for job-level status monitoring.
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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.