The correct answer is to create external tables on the JSON files using PolyBase, then use CREATE EXTERNAL TABLE AS SELECT (CETAS) to write transformed Parquet files. This approach minimizes data movement by leveraging PolyBase’s ability to read JSON data in place within Azure Data Lake Storage Gen2, while CETAS performs the transformation and writes the star schema tables back to the same storage layer as Parquet, all without copying data to an intermediate store. On the DP-203 exam, this scenario tests your understanding of how to use serverless SQL pool or dedicated SQL pool compute closest to the data, avoiding expensive data transfers. A common trap is selecting options that move data into Synapse tables first, which increases latency and cost. Remember the key principle: PolyBase for in-place reading, CETAS for in-place writing—think “read where it lives, write where it stays” to avoid unnecessary movement.
DP-203 Design and develop data processing Practice Question
This DP-203 practice question tests your understanding of design and develop data processing. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option D is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Trap categories for this question
Scenario analysis trap
SSIS is less optimal for this scenario.
Detailed technical explanation
How to think about this question
PolyBase uses the T-SQL OPENROWSET function with JSON format to read files directly from ADLS Gen2 via the ABFS driver, and CETAS writes the transformed data as Parquet files with predicate pushdown and columnar compression. Under the hood, PolyBase distributes the read workload across compute nodes, enabling parallel processing of large JSON datasets without moving data out of the lake. In real-world scenarios, this pattern is ideal for incremental ETL pipelines where raw JSON lands in the lake and must be converted to a star schema for downstream analytics.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Design and develop data processing — This question tests Design and develop data processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create external tables on the JSON files using PolyBase, then use CREATE EXTERNAL TABLE AS SELECT (CETAS) to write transformed Parquet files. — Option D is correct because 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.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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