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
Avro is a row-based serialisation format designed for write-heavy streaming and schema evolution, so serverless SQL cannot prune columns or push down filters, degrading analytical scans. It is tempting because it stores nested records compactly, making it a sound choice for Kafka pipelines or landing raw event data.
- ✓
Parquet
Why this is correct
Parquet stores data columnar with embedded schema and statistics, so Synapse serverless SQL reads only referenced columns and skips row groups via predicate pushdown. This satisfies the optimal query performance constraint for semi-structured JSON logs, whereas row-based formats force full scans and schema inference at runtime.
- ✗
CSV
Why it's wrong here
CSV is flat text with no schema or nested structure, so Synapse serverless SQL must parse every row and cannot exploit columnar pruning, hurting performance on JSON logs. It is tempting for simple tabular exports, where its universal readability and low tooling overhead make it a reasonable interchange choice.
- ✗
JSON
Why it's wrong here
Raw JSON forces serverless SQL to read and parse entire documents per query, with no columnar projection or predicate pushdown, so scanning stays expensive. It is tempting because it preserves the logs' native nested structure, which suits ingestion or archival where schema-on-read fidelity matters more than query speed.
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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