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DP-203 Develop data processing Practice Question

You are designing a data processing solution in Azure Data Factory that must process files as they arrive in Azure Blob Storage. The solution must trigger a pipeline automatically when a new file is created, and then run a Databricks notebook to process the file. You need to configure the trigger and the pipeline activity. Which two actions should you perform? (Choose two.)

⚠ Common exam trap

Watch out — candidates often confuse event-based triggers with scheduled triggers, and assuming any activity can run a Databricks notebook.

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 Databricks Notebook activity to the pipeline and configure it to pass the file path as a parameter.

To trigger a pipeline when a file is created in Blob Storage, you need an event-based trigger of type BlobCreated. To process the file with a Databricks notebook, you add a Databricks Notebook activity and pass the file path as a parameter. Tumbling window triggers are scheduled, not event-driven, and Copy or Stored Procedure activities do not run Databricks notebooks.

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 Databricks Notebook activity to the pipeline and configure it to pass the file path as a parameter.
Why this is correct
The Databricks Notebook activity in Azure Data Factory allows you to run a notebook in Azure Databricks. By passing the file path as a parameter, the notebook can process the specific file that triggered the pipeline. This is the correct activity to use for running a Databricks notebook in this scenario.
✓
Create an event-based trigger of type 'BlobCreated' and configure it to start the pipeline when a file is created.
Why this is correct
An event-based trigger in Azure Data Factory can listen for BlobCreated events from Azure Blob Storage. This allows the pipeline to start automatically when a new file arrives, meeting the requirement for event-driven processing. It is the correct trigger type for this scenario.
✗
Add a Copy activity to the pipeline to copy the file to a staging location before processing.
Why it's wrong here
While a Copy activity could be used to stage data, the requirement is to run a Databricks notebook to process the file. Adding a Copy activity is unnecessary and does not fulfill the need to execute a notebook. It would add complexity without addressing the core processing requirement.
✗
Add a Stored Procedure activity to the pipeline to call a stored procedure in Azure SQL Database.
Why it's wrong here
A Stored Procedure activity executes a stored procedure in Azure SQL Database, which is unrelated to running a Databricks notebook. This does not meet the requirement to process the file with a Databricks notebook. It would be appropriate only if the processing logic resided in a SQL database, which is not the case here.
✗
Create a tumbling window trigger and configure it to run every 5 minutes to check for new files.
Why it's wrong here
A tumbling window trigger runs on a scheduled basis, not in response to file creation events. While it can periodically check for new files, it introduces latency and does not provide immediate event-driven processing. This does not meet the requirement to trigger when a file is created.

Quick reference

Azure Blob Storage Tier Comparison

Tier	Storage Cost	Retrieval Cost	Latency	Use Case
Hot	Highest	Lowest	Immediate	Active data, frequent reads
Cool	Lower	Higher	Immediate	Data accessed < once / month
Cold	Lower still	Higher	Immediate	Data accessed < once / quarter
Archive	Lowest	Highest + rehydration delay	Hours	Long-term compliance retention

Go deeper

DP-203 Develop data processing Practice Question

Why each option matters

Option-by-option breakdown

Azure Blob Storage Tier Comparison

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