AZ-305 Design infrastructure solutions Practice Question
A large enterprise is designing a data analytics platform in Azure that will ingest terabytes of data daily from multiple sources, including IoT devices, social media feeds, and internal databases. The data must be stored in a raw format for future processing, and then transformed and aggregated for reporting. The company requires low-latency querying for real-time dashboards and the ability to run complex batch analytics using Spark. The solution must also provide a unified data governance layer for cataloging and lineage tracking. Which combination of Azure services should the company choose to meet all these requirements with minimal operational overhead?
⚠ Common exam trap
Many exam-takers choose Azure HDInsight or Azure Databricks for Spark processing, overlooking that Azure Synapse Analytics natively integrates Spark with SQL and governance via Purview, reducing operational overhead compared to managing separate clusters.
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 Data Lake Storage, Azure Synapse Analytics, and Microsoft Purview
Azure Data Lake Storage provides scalable, cost-effective raw storage for terabytes of daily data, Azure Synapse Analytics offers both low-latency querying for real-time dashboards and Spark-based batch analytics, and Microsoft Purview delivers a unified data governance layer with cataloging and lineage tracking. This combination minimizes operational overhead by integrating storage, compute, and governance into a single, managed platform.
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 Cosmos DB, Azure Stream Analytics, and Azure Analysis Services
Why it's wrong here
Azure Cosmos DB is a multi-model NoSQL database engineered for low-latency transactional access and global distribution, not for cost-effective landing and scanning of terabytes of raw analytics data; its request-unit pricing and per-index overhead make large-scale exploratory queries prohibitively expensive. Stream Analytics is a serverless event-processing engine that performs time-windowed streaming computations, so it has no role in batch analytics or historical data transformation. Analysis Services adds a tabular semantic model, but the stack lacks a unified data lake for raw files, a query engine that spans streaming and batch, and the automated data cataloging/lineage needed for enterprise governance.
- ✗
Azure Blob Storage, Azure HDInsight, and Azure Data Factory
Why it's wrong here
Blob Storage with a flat namespace lacks the hierarchical directory structure, POSIX permissions, and atomic rename semantics of Azure Data Lake Storage Gen2, so it is less suitable as a governed lakehouse foundation. HDInsight is a managed Hadoop/Spark cluster service that still requires you to configure and patch clusters, monitor node health, and wait for scale operations; unlike Synapse, it offers no native serverless SQL endpoint for direct interactive queries on the lake. While Data Factory can orchestrate pipelines, this trio leaves a gap in unified query--you must glue Blob, Hadoop jobs, and ADF together without the integrated data warehouse and Purview-based governance needed for a modern analytics platform.
- ✗
Azure SQL Database, Azure Databricks, and Azure Data Catalog
Why it's wrong here
Azure SQL Database is a relational OLTP system that enforces schema on write and has storage limits that make it impractical for landing raw, semi-structured files at data-lake scale; you would be forced to model unstructured data before you can explore it. Databricks gives you a strong Spark engine for batch processing and Delta Lake, but it does not by itself provide a fully managed enterprise data catalog with automated lineage scanning across multiple sources--that is the role of Microsoft Purview. Azure Data Catalog is a legacy service that Microsoft is replacing with Purview, so this stack also misses the centralized compliance, classification, and lineage capabilities required in a modern data estate.
- ✓
Azure Data Lake Storage, Azure Synapse Analytics, and Microsoft Purview
Why this is correct
Azure Data Lake Storage Gen2 supplies the required raw landing zone: it combines Blob scalability with a hierarchical namespace and granular POSIX ACLs, enabling governed storage from bytes to petabytes. Azure Synapse Analytics is the analytical core, offering serverless SQL to query raw ADLS files in place, dedicated pools for high-performance warehouse workloads, and built-in Spark for batch transformations--all without separate infrastructure to manage. Microsoft Purview completes the platform by automatically scanning these assets, classifying sensitive data, and capturing end-to-end lineage so downstream analytics can be audited and trusted. Together these services deliver storage, real-time and historical query, batch processing, and governance in one integrated solution.
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This AZ-305 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 AZ-305 exam.