DP-900 Describe an analytics workload on Azure Practice Question

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 Data Lake Storage Gen2

  Why it's wrong here

  Azure Data Lake Storage Gen2 is a highly scalable, cloud-optimized file storage service that provides a hierarchical namespace and POSIX-like access controls for storing vast amounts of raw or transformed data. However, it is purely a storage layer—it has no built-in query engine, indexing, or distributed compute to execute analytical SQL. Any analysis of data in the lake requires an external compute service such as Azure Synapse, Databricks, or HDInsight, so on its own it cannot satisfy the requirement of performing analytical queries directly.

  When this WOULD be correct

  A question asks for a cost-effective storage layer for raw sensor data that will be processed later by separate analytics tools, with no requirement for built-in query performance or complex aggregations directly on the stored data.

• ✗

  Azure SQL Database

  Why it's wrong here

  Azure SQL Database is a managed relational database service optimized for online transaction processing (OLTP), with row-based storage and B-tree indexing engineered for quick point reads and writes. Although columnstore indexes are available for some analytics, it lacks the massively parallel processing (MPP) architecture required to distribute heavy aggregation across thousands of nodes. Ingesting sensor data from thousands of IoT devices would quickly saturate its concurrency and I/O capacity, making multi-terabyte analytical queries impractical and cost-inefficient.

• ✓

  Azure Synapse Analytics dedicated SQL pool

  Why this is correct

  Azure Synapse Analytics dedicated SQL pool is a purpose-built enterprise data warehouse that uses massively parallel processing (MPP) to distribute tables across decoupled storage and compute nodes. Each node stores compressed columnar (columnstore) data, which minimizes I/O during large analytical scans and aggregations. The query optimizer breaks complex queries into parallel tasks operating across all nodes, so even petabyte-scale IoT telemetry can be joined, filtered, and aggregated efficiently. This architecture is exactly why it is the recommended service for large-scale analytical workloads in Azure.

• ✗

  Azure Cosmos DB

  Why it's wrong here

  Azure Cosmos DB is a globally distributed, multi-model NoSQL database engineered for sub-10-millisecond latency reads and writes in transactional applications, using a B-tree/column-based indexing strategy optimized for operational access patterns. It is not built for heavy analytical workloads: scanning millions of sensor records with complex aggregations would consume excessive request units (RUs) and become neither performant nor cost-effective. While Cosmos DB offers an analytical store, that capability is accessed through Azure Synapse Link, not natively through Cosmos DB's query interface, making it an incorrect choice for large-scale analytics.

  When this WOULD be correct

  An exam scenario where the requirement is for a globally distributed, low-latency, always-on NoSQL database for an IoT application that needs real-time data access and high throughput for point reads and writes, with no need for complex analytical queries.

Option-by-option analysis

Why each answer is right or wrong

Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.

Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”

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