DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You are designing a data ingestion pipeline for Azure Data Lake Storage Gen2 using Azure Databricks. The source is an on-premises SQL Server database with incremental changes captured via change data capture (CDC). The requirement is to ensure exactly-once semantics for each row while minimizing latency. Which approach should you recommend?
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
✓
Use Spark Structured Streaming in Azure Databricks to read CDC changes and write to Delta Lake.
Spark Structured Streaming in Azure Databricks, combined with Delta Lake, provides exactly-once semantics through checkpointing and transaction logs, and it is designed for low-latency streaming ingestion—ideal for CDC data. Option A is incorrect: Azure Data Factory with a tumbling window is a batch-based approach and cannot achieve exactly-once semantics for streaming data with low latency. Option B is incorrect: PolyBase is used for bulk loading data into Azure Synapse, not for streaming CDC ingestion. Option C is incorrect: Auto Loader and COPY INTO are designed for incremental batch loads of files, not for streaming row-level changes from a database.
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 Azure Data Factory with a tumbling window trigger to copy data every 5 minutes.
Why it's wrong here
This is batch, not streaming, and cannot guarantee exactly-once per row easily.
- ✗
Use PolyBase to create external tables and run T-SQL MERGE statements.
Why it's wrong here
PolyBase is for batch loading, not streaming.
- ✗
Use Azure Databricks Auto Loader with COPY INTO command.
Why it's wrong here
Auto Loader is for file ingestion, not CDC from databases.
- ✓
Use Spark Structured Streaming in Azure Databricks to read CDC changes and write to Delta Lake.
Why this is correct
Structured Streaming with Delta Lake ensures exactly-once and low latency.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
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.
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.
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