Watching a transformation job's health and progress
Monitoring data transformation in Microsoft Fabric involves tracking the execution, performance, and status of Dataflows Gen2, pipeline activities, and Spark notebook/job runs to ensure reliability and troubleshoot failures. Fabric provides centralized visibility through the Monitoring hub, item-level run history, and Spark application details, enabling engineers to identify bottlenecks, errors, and resource consumption.
Must-know
- The Monitoring hub in Fabric provides a unified view across the workspace showing run status, duration, and trigger type for pipelines, dataflows, and notebooks.
- Each Dataflow Gen2 run can be inspected individually to see refresh history, duration, and step-level errors, which helps isolate transformation failures.
- Pipeline activity runs expose detailed input/output JSON, error messages, and execution duration per activity, which is essential for debugging Copy or Dataflow activities embedded in pipelines.
- Spark job monitoring for notebooks and Spark job definitions surfaces the Spark application UI, including stages, tasks, executors, and logs for diagnosing performance issues or failures in transformation code.
- Fabric retains run history for a limited time (subject to workspace/capacity settings), so long-term monitoring or auditing typically requires exporting logs or integrating with Azure Monitor/Log Analytics.
- Failures in scheduled dataflow or pipeline runs can be configured to send notifications (e.g., via Fabric alerts or Power Automate/Outlook integration), so learners should know how to set up proactive alerting rather than relying solely on manual checks.
A data engineering team runs several Dataflow Gen2 and notebook-based transformations across multiple workspaces in the same Fabric capacity. To determine which transformation activities consumed the most capacity units (CUs) over the past week, so they can prioritize which ones to optimize first, which tool should the team use?
What you have tried across DP-700's objectives, not a readiness score.
Implement and manage an analytics solution
- Tuning a workspace's Spark compute defaults and pool sizing
- Grouping and governing workspaces with a Fabric domain
- Setting per-workspace defaults for OneLake storage
- Standing up an Airflow job runtime inside a workspace
- Connecting a workspace to a Git repository
- Managing schema changes with a database project
- Promoting Fabric items across environments with a deployment pipeline
- Granting and restricting access at the workspace level
- Locking down who can open a single Fabric item
- Layering row, column, object, and file-level security rules
- Hiding sensitive column values behind a dynamic mask
- Classifying Fabric items with a sensitivity label
- Marking a trusted item as promoted or certified
- Reading a Fabric audit log to see who did what
- Securing data at the OneLake storage layer
- Picking the right build tool among a dataflow, a pipeline, and a notebook
- Kicking off a job on a schedule or in response to an event
- Chaining notebooks and pipelines together with parameters and dynamic expressions
Ingest and transform data
- Deciding between a full reload and an incremental load
- Shaping source data ahead of a dimensional-model load
- Landing a continuous stream of data into storage
- Matching a workload to the right Fabric data store
- Picking a transformation tool from dataflows, notebooks, KQL, or T-SQL
- Linking to external data without copying it via a OneLake shortcut
- Keeping a source database continuously replicated into Fabric
- Moving data into Fabric with a data pipeline
- Writing transform logic in PySpark, SQL, or KQL
- Flattening related tables into one wide, denormalized shape
- Rolling records up with group-by aggregations
- Dealing with duplicate rows, gaps, and data that arrives late
- Selecting the right engine for a real-time workload
- Weighing storage-in-place against a linked shortcut for a Real-Time Intelligence table
- Weighing an accelerated shortcut against a standard one for query speed
- Routing and reshaping live events with an Eventstream
- Handling a continuous flow of records with Spark's structured streaming
- Querying and reshaping event data with KQL
- Aggregating a stream over sliding or tumbling time windows
Monitor and optimize an analytics solution
- Watching an ingestion job's health and progress
- Watching a transformation job's health and progress
- Tracking whether a semantic model's refresh actually succeeded
- Setting up an alert to catch a failure early
- Tracking down why a pipeline run failed and fixing it
- Diagnosing why a dataflow run failed
- Debugging a notebook run that failed
- Troubleshooting a misbehaving Eventhouse
- Troubleshooting a misbehaving Eventstream
- Debugging a T-SQL statement that failed
- Fixing a broken or unreachable shortcut
- Speeding up a Lakehouse table with maintenance operations
- Making a slow pipeline run faster
- Tuning a Fabric warehouse for faster queries
- Improving throughput on real-time streaming components
- Tuning a Spark job to run faster and cheaper
- Making a slow query run faster
Coverage checked against the published exam guide on Aug 11, 2026.
These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.