DP-203 Develop data processing Practice Question
You need to transform semi-structured JSON data into a tabular format for analysis in Azure Synapse Analytics. The data is stored in ADLS Gen2. Which feature should you use to query the JSON data directly without loading it into a table?
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 the OPENROWSET function in a serverless SQL pool.
OPENROWSET in Synapse serverless SQL can query JSON files directly. Option B (Azure Data Factory) flattens JSON but is an orchestration tool, not a direct query method. Option C (PolyBase) requires external tables. Option D (COPY INTO) loads data into a table, not direct query.
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 OPENROWSET function in a serverless SQL pool.
Why this is correct
OPENROWSET in a serverless SQL pool queries JSON files directly from ADLS Gen2, using WITH clauses to shred semi-structured documents into relational columns. This satisfies the stem's constraint of querying without loading into a table, since serverless pools read files in place and charge only for data processed.
- ✗
Use Azure Data Factory to flatten the JSON and store as Parquet.
Why it's wrong here
Data Factory copies and reshapes data into Parquet, which is a load-and-materialise pipeline, not direct querying of the JSON in place. It suits scheduled ETL into curated storage. The requirement is querying the JSON directly from ADLS Gen2 without loading it into a table.
- ✗
Create an external table using PolyBase.
Why it's wrong here
PolyBase external tables in a dedicated SQL pool read delimited or Parquet files; JSON is not a supported external file format, so the semi-structured data cannot be queried directly. PolyBase suits querying CSV or Parquet in ADLS Gen2 without loading.
- ✗
Use the COPY INTO command in a dedicated SQL pool.
Why it's wrong here
COPY INTO ingests data into a dedicated SQL pool table, which is precisely the load the scenario excludes, and it does not query JSON in place. It suits high-throughput bulk loading from ADLS Gen2 into distributed tables for later T-SQL analysis.
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 |
Go deeper
Related to this question
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Implement Azure Stream Analytics
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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