DP-900 Describe core data concepts 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.

• ✗

  A. Workload A: Azure SQL Database; Workload B: Azure Cosmos DB

  Why it's wrong here

  Azure SQL Database is indeed a strong OLTP engine with rowstore indexing and transactional consistency, so Workload A would be adequately served. However, Workload B appears to demand large-scale analytics over huge volumes, and Cosmos DB is not architected for that: it lacks a query optimizer for complex joins and aggregations, and its request-unit pricing model assumes point reads/writes rather than heavy scans. Even with the analytical store, Cosmos DB's analytics are typically offloaded to Synapse, so using it directly for Workload B is a mismatch.

  When this WOULD be correct

  If Workload B involved real-time analytics on high-velocity, globally distributed data with low-latency requirements, Azure Cosmos DB could be correct. For example, a scenario where both workloads need low-latency, globally distributed access and Workload B is a real-time dashboard on streaming data.

• ✓

  B. Workload A: Azure Cosmos DB; Workload B: Azure Synapse Analytics

  Why this is correct

  Azure Cosmos DB is a multi-model NoSQL database engineered for single-digit-millisecond write/read latency and instant global distribution, making it the right fit for Workload A's transaction-intensive, low-latency requirements (OLTP). Azure Synapse Analytics is a massively parallel processing (MPP) data warehouse with columnar storage and distributed query execution, built specifically for petabyte-scale analytical scans and complex aggregations (OLAP). This pairing cleanly separates transactional and analytical concerns, so each service is applied where its architecture provides the most benefit.

• ✗

  C. Workload A: Azure Synapse Analytics; Workload B: Azure SQL Database

  Why it's wrong here

  Reversing the services makes both workloads wrong. Synapse Analytics' MPP engine and columnstore are optimized for batch scanning and aggregating enormous data sets, but they are not designed for high-volume, single-row, low-latency transactions; a single-point lookup would incur query coordination overhead that defeats OLTP requirements. Conversely, Azure SQL Database, while it supports columnstore indexes, is fundamentally a row-based OLTP database that cannot match the distributed scale-out and compression of a dedicated data warehouse for petabyte-scale aggregations. Thus neither workload receives a service suited to its pattern.

  When this WOULD be correct

  This option would be correct if Workload A required complex analytics on large datasets (e.g., real-time dashboards) and Workload B involved standard OLTP with moderate data volumes (e.g., a customer database).

• ✗

  D. Workload A: Azure Cosmos DB; Workload B: Azure Cosmos DB

  Why it's wrong here

  Putting both on Cosmos DB fails Workload B because although Cosmos DB offers an analytical store and Synapse Link for near-real-time analytics, it is not a replacement for a dedicated MPP warehouse when the load involves complex multi-table joins and terabyte-scale aggregation scans. Running such analytics directly on Cosmos DB would either require inefficient cross-partition queries or compete with the OLTP traffic for request units, harming Workload A's latency. A purpose-built service like Azure Synapse Analytics would provide columnar compression, materialized views, and distributed query execution that Cosmos DB's engine lacks.

  When this WOULD be correct

  This option would be correct if both workloads required globally distributed, low-latency access to data with flexible schemas, and the analytical queries could be handled by Cosmos DB's built-in analytical store or Synapse Link. For example, a real-time analytics application needing both transactional and analytical capabilities on the same data.

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?”