Debugging a notebook run that failed
In Microsoft Fabric, notebook errors typically surface from Spark job execution issues, code/syntax problems, or resource constraints, and can be diagnosed using the cell-level error output, Spark job monitoring, and the Fabric Monitoring hub. Effective troubleshooting relies on reading stack traces, checking Spark application logs, and correlating notebook runs with underlying Spark session and pool metrics.
Must-know
- Each notebook cell execution error displays an inline error message with a stack trace; expanding it often reveals the root Spark exception (e.g., AnalysisException, OutOfMemoryError, or Py4JJavaError).
- The Monitoring hub in Fabric lists all notebook/Spark application runs with status (Succeeded, Failed, Cancelled) and allows drill-down into Spark UI details, logs, and driver/executor diagnostics for failed runs.
- Common causes of notebook failures include schema mismatches, missing/incorrect library installations, session timeouts, insufficient Spark pool resources, and lakehouse/table path or permission errors.
- The Spark application detail view (accessible from a notebook run or the Monitoring hub) provides access to driver and executor logs, which are essential for diagnosing memory, shuffle, or executor-lost failures not visible in the notebook cell output itself.
- High-level severity issues like session expiration or pool capacity exhaustion often require adjusting session timeout settings, Spark pool sizing, or node/executor configuration rather than fixing code.
- Re-running a failed cell or notebook after a transient error (e.g., a temporary lakehouse connectivity or throttling issue) can resolve it, but recurring failures should be investigated via logs before simply retrying.
A data engineer builds and tests a notebook in a development workspace that reads files using a relative path such as Files/raw/orders.csv, relying on the lakehouse attached to the notebook. After using deployment pipelines to promote the notebook to a test workspace, running the notebook produces an error stating the specified path cannot be found, even though the equivalent lakehouse and files already exist in the test workspace. What is the most likely cause, and how should the engineer resolve it?
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.