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DP-203 Practice Question: A company uses Azure Synapse Analytics to process…
A company uses Azure Synapse Analytics to process large datasets. They need to transform JSON data stored in Azure Data Lake Storage Gen2 into a star schema. Which data processing approach minimizes data movement and leverages the compute closest to the data?
⚠ Common exam trap
Candidates often assume Spark notebooks (Option C) are always the best for JSON transformation, but PolyBase with CETAS is more efficient for minimizing data movement because it processes data in-place using SQL compute without loading entire datasets into memory.
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 on the JSON files using PolyBase, then use CREATE EXTERNAL TABLE AS SELECT (CETAS) to write transformed Parquet files.
It uses PolyBase external tables and CETAS to transform JSON data directly in Azure Data Lake Storage Gen2, minimizing data movement by leveraging the compute power of the dedicated SQL pool or serverless SQL pool closest to the data. This approach reads JSON in place, transforms it into Parquet format, and writes the star schema tables back to the data lake without copying data to an intermediate store.
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 to copy the JSON data into Azure SQL Database, then use T-SQL to transform.
Why it's wrong here
Copies data unnecessarily.
- ✗
Use Azure Data Factory with SSIS to transform and load into dedicated SQL pool.
Why it's wrong here
SSIS is less optimal for this scenario.
- ✗
Load data into a Spark DataFrame in Synapse notebooks, transform, and write back.
Why it's wrong here
Loads data into memory, causing movement.
- ✓
Create external tables on the JSON files using PolyBase, then use CREATE EXTERNAL TABLE AS SELECT (CETAS) to write transformed Parquet files.
Why this is correct
Minimizes movement by querying in place.
Visual reference
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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