DP-203 Design and implement data storage Practice Question
A data engineer needs to store semi-structured JSON logs from multiple sources in Azure. The logs must be queryable using T-SQL and support schema-on-read. Which Azure service should be used?
⚠ Common exam trap
Test-takers frequently confuse schema-on-read with schema-on-write, picking Azure SQL Database or Cosmos DB because they support JSON, but those require predefined schemas or containers, failing the schema-on-read requirement.
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
✓
Azure Synapse serverless SQL pool with JSON files in ADLS Gen2.
Azure Synapse serverless SQL pool can query JSON files stored in ADLS Gen2 using T-SQL, supporting schema-on-read by inferring the schema from the file content at query time. This makes it ideal for semi-structured logs that need to be queried without predefined schema.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Azure Synapse serverless SQL pool with JSON files in ADLS Gen2.
Why this is correct
Synapse serverless SQL pool queries JSON files in ADLS Gen2 using T-SQL with OPENROWSET, applying schema-on-read so no ingestion or schema definition is required. This satisfies both the T-SQL query requirement and the schema-on-read constraint for semi-structured logs.
- ✗
Azure Data Factory mapping data flows.
Why it's wrong here
Mapping data flows are a transformation engine inside Azure Data Factory pipelines; they process data during execution and store nothing, so they cannot serve as the queryable repository the stem demands. They are tempting because they handle JSON transformations visually, which suits ETL orchestration rather than persistent storage.
- ✗
Azure Cosmos DB Core (SQL) API.
Why it's wrong here
Cosmos DB Core (SQL) API queries documents with its own SQL dialect, not T-SQL, and its schema-agnostic container model does not deliver the T-SQL surface the stem requires. It is tempting because it stores JSON natively at scale, which suits globally distributed operational workloads rather than analytical T-SQL querying.
- ✗
Azure SQL Database with JSON columns.
Why it's wrong here
Azure SQL Database requires a predefined schema and parses JSON only through OPENJSON or JSON_VALUE functions, so it cannot provide schema-on-read over raw semi-structured logs. It is tempting because it offers genuine T-SQL, which fits relational workloads with structured tables and occasional JSON columns.
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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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.