Tracking down why a pipeline run failed and fixing it

Microsoft Fabric Data Factory pipelines provide monitoring and diagnostic tools to identify why a pipeline run or activity failed, using run history, output logs, and error codes. Resolving pipeline errors typically involves inspecting activity-level error details, adjusting retry/timeout settings, and using pipeline features like Fail activity, If Condition, and Set Variable for better error handling and diagnostics.

Must-know

• The Monitor hub in Fabric lets you view pipeline run history, filter by status (Succeeded, Failed, In Progress), and drill into individual activity runs to see input, output, and error messages.
• Each failed activity returns an error code, message, and often a 'failureType' (UserError vs SystemError) that helps distinguish configuration/data issues from transient service issues.
• You can configure Retry and Retry interval settings on activities to automatically handle transient failures like network timeouts or throttling errors without manual intervention.
• Use the 'On Failure' output path from an activity (in the pipeline canvas) to build conditional error-handling logic, such as sending a Teams/email notification or logging failure details to a table.
• Common failure causes include invalid connection credentials, schema drift between source and destination, insufficient permissions on a workspace/lakehouse, and expired or misconfigured linked service/connection details.
• Activity timeout settings should be reviewed carefully since long-running Copy or Notebook activities can fail simply due to a timeout value that's too low rather than a true data or logic error.

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.