DP-203 Develop data processing Practice Question
You are designing a data processing solution that must handle both batch and stream processing in a unified manner. You need to choose a technology that supports a single code base for both processing modes. Which TWO technologies meet this requirement?
⚠ Common exam trap
Many exam-takers confuse Azure Stream Analytics as a unified solution because it handles streaming, but they overlook that it lacks native batch processing support, whereas Apache Spark Structured Streaming (in Databricks or Synapse) truly enables a single code base for both modes.
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
✓
Azure Databricks with Delta Live Tables
Azure Databricks with Delta Live Tables (DLT) supports both batch and stream processing using a single code base by allowing you to define streaming and batch data sources declaratively in the same pipeline. DLT automatically handles incremental processing for streaming and full refreshes for batch, enabling unified logic without separate code paths.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Azure Databricks with Delta Live Tables
Why this is correct
Delta Live Tables supports both batch and streaming with a unified declarative pipeline.
- ✗
Azure Stream Analytics
Why it's wrong here
Stream Analytics is a streaming-only service; it does not handle batch processing.
- ✗
Azure Data Factory
Why it's wrong here
Data Factory is primarily batch-oriented; it does not support streaming natively.
- ✗
Azure SQL Database with Change Tracking
Why it's wrong here
Change Tracking is for change data capture, not a processing engine for batch/stream.
- ✓
Apache Spark Structured Streaming in Azure Synapse
Why this is correct
Structured Streaming can run both batch and streaming jobs using the same DataFrame/DataSet API.
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