DP-203 Develop data processing Practice Question
You have an Azure Databricks notebook that processes a large Delta table and must be orchestrated from Azure Data Factory on a schedule. The notebook accepts two parameters, the source path and a run date. You need the pipeline to pass these values at runtime and to surface notebook failures as pipeline failures. Which activity configuration should you use?
⚠ Common exam trap
The trap here is reaching for the Jobs API through a Web activity, which submits work asynchronously and does not translate notebook failure into pipeline failure without extra polling.
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
✓
A Databricks Notebook activity with base parameters defined as key-value pairs bound to pipeline parameters.
The Databricks Notebook activity is the native orchestrator for running an existing notebook from Data Factory. Base parameters passed as key-value pairs become notebook widgets and can be bound to pipeline parameters, and the activity reports the notebook run outcome so a failed notebook fails the pipeline run as required.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
An Azure Function activity that triggers the notebook through a Databricks personal access token.
Why it's wrong here
An Azure Function activity invokes a function app and returns its response; the notebook run status is not part of that response unless the function implements polling and error translation. This introduces an extra component and custom code, and the pipeline would not natively treat a notebook failure as its own failure, so the requirement is not satisfied cleanly.
- ✗
A Web activity that calls the Databricks Jobs API to submit a one-time run with notebook parameters.
Why it's wrong here
A Web activity can invoke the Jobs API and pass parameters, but it returns only the API response, not the notebook run result. The pipeline would need extra polling logic to detect notebook failure, and a submitted run can continue asynchronously after the activity succeeds. This adds complexity and does not reliably surface notebook failures as pipeline failures.
- ✓
A Databricks Notebook activity with base parameters defined as key-value pairs bound to pipeline parameters.
Why this is correct
The Databricks Notebook activity supports base parameters that are passed to the notebook as widgets, and those values can be bound to pipeline parameters or expressions. Failures returned by the notebook propagate to the activity and fail the pipeline run, satisfying both the parameter-passing and error-surfacing requirements in a single activity configuration.
- ✗
A Databricks Jar activity that reads parameters from environment variables set on the cluster.
Why it's wrong here
A Jar activity runs compiled code rather than the existing notebook, so the notebook logic would have to be repackaged and redeployed. Cluster environment variables are not a supported parameter channel for this activity and would not carry the run date through the pipeline. Failures would surface, but the parameterization approach does not meet the stated requirement.
Go deeper
Related to this question
Learn chapter
Implement Azure Data Factory Pipelines
Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
Key term
Azure Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
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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
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