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DP-203 Practice Question: Designing a data pipeline that ingests JSON files…
You are designing a data pipeline that ingests JSON files from Azure Blob Storage into Azure Synapse Analytics using PolyBase. The files contain nested JSON arrays. What should you do to ensure that the data is loaded correctly?
⚠ Common exam trap
Candidates often assume PolyBase can handle any JSON structure natively, but it only supports flat JSON files, and they overlook the need for pre-processing nested arrays with tools like Data Factory or 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
✓
Flatten the JSON arrays into a tabular format using Azure Data Factory or Databricks before loading.
PolyBase in Azure Synapse Analytics cannot directly handle nested JSON arrays; it requires a flat, tabular structure. Azure Data Factory or Databricks can flatten the nested arrays into rows and columns before loading, ensuring compatibility with PolyBase's external table format.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Flatten the JSON arrays into a tabular format using Azure Data Factory or Databricks before loading.
Why this is correct
PolyBase requires tabular data, so flattening is necessary.
- ✗
Create an external table with the JSON file type and use a schema definition.
Why it's wrong here
PolyBase external tables support only delimited text and Parquet.
- ✗
Use the OPENJSON function in T-SQL to parse the JSON during the load.
Why it's wrong here
OPENJSON can be used in Synapse, but PolyBase cannot use it for external tables.
- ✗
Use PolyBase with a JSON format file specifying the schema.
Why it's wrong here
PolyBase does not support JSON files directly.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
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.
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.