DP-203 Develop data processing Practice Question
Your organization has an Azure Data Factory pipeline that executes a series of activities to transform data. One of the activities is an Azure Databricks notebook that should run only if the previous activity succeeds. You need to configure the pipeline to handle failures gracefully and send an email alert if the Databricks activity fails. What should you do?
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
✓
Add a failure output path from the Databricks activity to a Web activity that calls an email API.
You can configure a failure output path from the Databricks activity and attach a Web activity that calls an email API to send an alert when the Databricks activity fails. Option B is incorrect because retry and timeout settings do not send email alerts. Option C is incorrect because a Schedule trigger does not handle activity-level failures directly. Option D is incorrect because setting a dependency to 'Succeeded' only runs subsequent activities on success, not on failure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add a failure output path from the Databricks activity to a Web activity that calls an email API.
Why this is correct
You can route failure output to a Web activity to send an email via Logic Apps or Azure Functions.
- ✗
Configure a retry policy and a timeout for the Databricks activity.
Why it's wrong here
Retry and timeout help with transient failures but do not send alerts.
- ✗
Use a Schedule trigger to run the pipeline and check for failures using Azure Monitor.
Why it's wrong here
Schedule trigger does not handle failure alerts directly.
- ✗
Set a dependency condition on the Databricks activity to 'Succeeded' and add a Send Email activity on the success path.
Why it's wrong here
This would send email on success, not on failure.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
Key term
Data Transformation Pipelines
Data transformation pipelines are automated sequences of steps that take raw data from a source, clean and reshape it into a usable format, and then load it into a destination for analysis or storage.
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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