DP-203 Develop data processing Practice Question
You are designing a data lakehouse architecture in Azure using Delta Lake. The solution needs to process batch and streaming data from multiple sources, including IoT devices and CRM systems. You need to ensure data quality by enforcing schema validation and handling schema evolution. You also need to provide a unified catalog for querying. Which service should you use?
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 Databricks Unity Catalog
Azure Databricks Unity Catalog provides a unified governance solution for data and AI, including schema enforcement and evolution for Delta Lake. Option A is wrong because Azure Purview is for data discovery and lineage, not for schema enforcement. Option B is wrong because Azure Data Lake Storage Gen2 is storage only, not a catalog or governance layer. Option C is wrong because Azure Synapse Analytics serverless SQL pool is a query engine and does not provide the same schema management features as Unity Catalog.
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 Purview
Why it's wrong here
Azure Purview is for data discovery, lineage, and governance, but it does not enforce schemas or handle schema evolution within Delta Lake.
- ✗
Azure Data Lake Storage Gen2
Why it's wrong here
Azure Data Lake Storage Gen2 is a scalable storage service; it does not provide schema enforcement or a unified catalog for querying.
- ✗
Azure Synapse Analytics serverless SQL pool
Why it's wrong here
Azure Synapse Analytics serverless SQL pool can query Delta tables, but it lacks the schema governance and catalog features that Unity Catalog offers.
- ✓
Azure Databricks Unity Catalog
Why this is correct
Azure Databricks Unity Catalog provides a central catalog with schema enforcement and evolution for Delta Lake, making it the correct choice for a data lakehouse with batch and streaming data.
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
Learn chapter
Introduction to Azure Data Engineering
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.
Key term
Azure Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
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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.