DP-203 Develop data processing Practice Question
You are designing a data processing solution for a retail company that uses Azure Databricks. The solution needs to process streaming sales data from Event Hubs and batch data from Azure Data Lake Storage Gen2. You need to ensure that the solution can handle late-arriving data and maintain exactly-once semantics. Which TWO technologies should you use?
⚠ Common exam trap
Watch out — candidates often confuse Azure Stream Analytics with Databricks Structured Streaming, assuming both can achieve exactly-once semantics with Delta Lake, but Stream Analytics does not write directly to Delta tables and lacks the transactional guarantees needed for idempotent late-arriving data processing in Databricks.
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
✓
Delta Lake
Delta Lake is correct because it provides ACID transactions, schema enforcement, and time travel capabilities, which are essential for handling late-arriving data and ensuring exactly-once semantics when combined with Structured Streaming. It allows you to merge late records into existing Delta tables using merge operations (upserts) without corrupting the data state.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Delta Lake
Why this is correct
Provides ACID transactions and supports exactly-once semantics.
- ✓
Azure Databricks Structured Streaming
Why this is correct
Supports watermarking and event-time processing for late data.
- ✗
PolyBase
Why it's wrong here
Used for batch loading, not streaming.
- ✗
Azure Stream Analytics
Why it's wrong here
Does not provide native exactly-once semantics with Delta Lake.
- ✗
Azure Data Factory
Why it's wrong here
Not designed for streaming processing.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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