DP-203 Design and implement data storage Practice Question
A data engineer needs to store semi-structured JSON logs for analysis using Azure Synapse Serverless SQL. Which file format should be used for optimal query performance?
⚠ Common exam trap
Test-takers frequently assume semi-structured data must stay in its native JSON format for simplicity, overlooking that columnar formats like Parquet can natively store nested JSON structures via repeated fields and maps, while providing massive performance gains in serverless SQL engines.
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
✓
Parquet
Parquet is correct because it is a columnar storage format that enables predicate pushdown and compression, significantly reducing the amount of data scanned by Azure Synapse Serverless SQL for analytical queries on semi-structured JSON logs. This format aligns with the engine's design for high-performance read operations on large datasets, unlike row-oriented formats that require full file scans.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Avro
Why it's wrong here
Row-based; not ideal for analytical queries.
- ✓
Parquet
Why this is correct
Columnar, compressed, optimized for query performance.
- ✗
CSV
Why it's wrong here
Row-oriented; less efficient for analytics.
- ✗
JSON
Why it's wrong here
Verbose; not columnar.
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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