DP-203 Develop data processing Practice Question
You are designing a data processing solution in Azure Synapse Analytics. The solution must support incremental loading of data from an Azure SQL Database to a dedicated SQL pool using PolyBase. Which approach should you use to minimize data movement and maximize performance?
⚠ Common exam trap
Test-takers frequently assume external tables are only for static data or Hadoop, but PolyBase in Synapse supports external tables against Azure SQL Database for efficient incremental loading, making options that introduce extra hops (like Data Factory or bcp) seem more familiar but less optimal.
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
✓
Create external tables in the dedicated SQL pool that reference the source data, then use CREATE TABLE AS SELECT (CTAS) to load incrementally.
Using external tables with PolyBase in Azure Synapse Analytics allows you to directly query the source Azure SQL Database without moving the data first. The CREATE TABLE AS SELECT (CTAS) statement then loads only the incremental data into the dedicated SQL pool, minimizing data movement by leveraging PolyBase's parallel streaming capability for maximum performance.
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 bcp utility to export data from Azure SQL Database to a text file, then bulk insert into the dedicated SQL pool.
Why it's wrong here
bcp does not support incremental loading and requires manual file management.
- ✓
Create external tables in the dedicated SQL pool that reference the source data, then use CREATE TABLE AS SELECT (CTAS) to load incrementally.
Why this is correct
PolyBase external tables enable direct query of source data, and CTAS allows efficient incremental loading with minimal data movement.
- ✗
Use Azure Data Factory with a copy activity to load data into staging tables, then merge into the target table.
Why it's wrong here
Using ADF copy activity adds overhead of moving data through an intermediate staging area.
- ✗
Use Azure Databricks to read the source data, apply transformations, and write to the dedicated SQL pool using the Spark connector.
Why it's wrong here
Azure Databricks introduces an additional compute layer, increasing complexity and latency.
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