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DP-203 Practice Question: A data engineer is designing a batch processing…
A data engineer is designing a batch processing pipeline that reads data from Azure Blob Storage, transforms it using Azure Databricks, and writes the output to Azure Synapse Analytics. The source files are in CSV format and arrive daily at 02:00 UTC. The transformation must be idempotent and the pipeline should handle late-arriving data (up to 2 hours). What is the best approach to trigger the pipeline?
⚠ Common exam trap
Microsoft often tests the distinction between schedule triggers (fixed time) and tumbling window triggers (window-based with late arrival handling), where candidates mistakenly choose a simple schedule trigger because they overlook the late-arriving data requirement.
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
✓
Tumbling window trigger with window size of 1 day and a late arrival window of 2 hours
A tumbling window trigger in Azure Data Factory allows you to define a fixed-size window (1 day) and a late arrival window (2 hours), which ensures idempotent processing by automatically rerunning the window for late-arriving data within the specified delay. This matches the requirement for daily batch processing at 02:00 UTC while handling data arriving up to 2 hours late.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Storage event trigger using Azure Event Grid
Why it's wrong here
Similar to event trigger, may cause duplicate processing.
- ✗
Schedule trigger set to 02:00 UTC daily
Why it's wrong here
Does not handle late-arriving data.
- ✓
Tumbling window trigger with window size of 1 day and a late arrival window of 2 hours
Why this is correct
Ensures idempotency and handles late data by allowing up to 2 hours delay.
- ✗
Event trigger on blob creation in the container
Why it's wrong here
May fire multiple times for the same file and requires deduplication logic.
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
Related to this question
Learn chapter
Introduction to Azure Data Engineering
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 by Johnson Ajibi, MSc IT Security
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