DP-203 Develop data processing Practice Question
You are developing an Azure Databricks notebook that processes streaming data from Azure Event Hubs using Structured Streaming. The stream writes to a Delta Lake table. You need to ensure that the stream can recover from failures and continue processing from where it left off without reprocessing all data. You also need to minimize the impact on the source. What should you configure?
⚠ Common exam trap
The trap here is thinking that Delta Lake alone provides fault tolerance, when in fact checkpointing must be configured separately to track streaming progress and enable recovery.
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
✓
Set the checkpoint location to a path in Azure Data Lake Storage Gen2 and use the Delta table as the sink.
Checkpointing is the mechanism that enables Structured Streaming to recover from failures by storing progress information in durable storage. Combining a checkpoint location in Azure Data Lake Storage Gen2 with a Delta Lake sink ensures exactly-once processing and allows the stream to resume from the last offset. Other options either lack checkpointing or introduce unnecessary complexity and source load.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the foreachBatch sink and manually store the offset in an Azure SQL Database.
Why it's wrong here
While foreachBatch allows custom logic per micro-batch, manually managing offsets is complex and error-prone. Structured Streaming already provides built-in checkpointing that handles offset management reliably. This approach adds unnecessary complexity and does not inherently minimize impact on the source; it also requires additional infrastructure and code to ensure exactly-once processing.
- ✓
Set the checkpoint location to a path in Azure Data Lake Storage Gen2 and use the Delta table as the sink.
Why this is correct
Structured Streaming uses checkpointing to track progress and recover from failures. Setting a checkpoint location in durable storage like Azure Data Lake Storage Gen2 allows the stream to resume from the last committed offset after a restart. Writing to a Delta table provides exactly-once semantics when combined with checkpointing, ensuring no data loss or duplication. This configuration meets both recovery and minimal source impact requirements.
- ✗
Enable auto-compaction on the Delta table and set the trigger to continuous.
Why it's wrong here
Auto-compaction optimizes file sizes in Delta Lake but does not provide fault tolerance or offset tracking. A continuous trigger runs the stream continuously, which can increase load on the source and does not by itself ensure recovery from failures. Without a checkpoint location, the stream cannot resume from the last processed offset, leading to potential data loss or reprocessing.
- ✗
Set the startingOffsets option to earliest and enable watermarking.
Why it's wrong here
Setting startingOffsets to earliest causes the stream to reprocess all available data from the beginning on each restart, which increases load on the source and violates the requirement to continue from where it left off. Watermarking handles late data in aggregations but does not provide checkpoint-based recovery. This configuration would lead to repeated processing and higher source impact.
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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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