A dataflow refresh fails after a source column is renamed. What is the fix?
Why C is the answer
Update the query step that refers to the old name
Steps reference column names, so the renamed column must be updated in the query.
In detail
- The query still refers to the old name.
- Update that step.
- Agree schema changes with source owners.
Use case
An illustrative scenario, not a real incident.
A source renames cust_id to customer_id, and a refresh fails. Updating the step that names the old column fixes it.
See it in different ways
flowchart TD
A["Monitor hub"] --> B{"Which item failed?"}
B -->|"Pipeline"| C["Activity error output"]
B -->|"Notebook"| D["Spark logs"]
B -->|"Capacity pressure"| E["Capacity Metrics app"]mindmap
root((Monitoring))
Activities
Monitor hub
Capacity
Capacity Metrics app
Warehouse
Query insights
Spark
Logs
Copy step
Run detailsflowchart LR S0["Refresh fails"] --> S1["Find the failing step"] --> S2["Use the new name"] --> S3["Refresh again"]
Key point: Query steps reference column names.
Why the other options are wrong
- A. Add a shortcut to a different lakehouse
- A shortcut does not fix the query.
- B. Turn off sensitivity labels for the item
- Labels are unrelated.
- D. Increase the capacity size for the workspace
- Capacity does not fix a missing column.