DP-900 Describe core data concepts Practice Question
Which THREE factors should you consider when choosing between Azure SQL Database and Azure Cosmos DB for a new application? (Choose three.)
⚠ Common exam trap
Many exam-takers confuse cost or storage limits as key differentiators, but the DP-900 exam focuses on schema flexibility, global distribution, and consistency models as the core architectural trade-offs between these two services.
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
✓
Schema flexibility
Option A (Schema flexibility) is correct because Azure Cosmos DB is schema-agnostic and stores JSON documents without a fixed schema, whereas Azure SQL Database requires a predefined relational schema with tables, columns, and data types, so the degree of schema flexibility needed directly drives the choice. Option B (Global distribution needs) is correct because Cosmos DB offers turnkey multi-region writes and reads with single-digit-millisecond latency and multiple well-defined consistency levels across any Azure region, while Azure SQL Database's geo-replication (active geo-replication, auto-failover groups) is more limited and primarily read-scale oriented. Option E (Consistency model requirements) is correct because Cosmos DB lets you choose among five consistency levels (strong, bounded staleness, session, consistent prefix, eventual) per account, whereas Azure SQL Database provides strong consistency via ACID transactions, so applications needing tunable consistency favor Cosmos DB. Option C (Cost per GB) is not a primary architectural decision factor here since both services have varied pricing models (DTU/vCore vs. RU/s and storage) and cost alone does not determine which engine fits the workload. Option D (Maximum data size) is not a distinguishing factor because both services scale to very large datasets (Azure SQL Database up to 100 TB with Hyperscale, Cosmos DB virtually unlimited), so it rarely decides between them.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Schema flexibility
Why this is correct
Cosmos DB automatically indexes every property of JSON documents, so you can evolve an item's shape without ALTER TABLE statements or migration scripts; a SQL database schema is set up front through DDL and any change requires locks and downtime planning. If your workloads are heterogeneous or changing rapidly, this flexibility is a decisive factor, whereas a relational schema enforces rigid structure and integrity.
- ✓
Global distribution needs
Why this is correct
Cosmos DB is designed as a globally distributed database service, letting you replicate data to any number of Azure regions and write to multiple regions simultaneously while maintaining SLAs for availability and latency. SQL Database requires you to manually set up active geo-replication and failover groups, and multi-master writes are not supported. If your user base spans continents, this native replication strategy is a key architectural consideration.
- ✗
Cost per GB
Why it's wrong here
Although storage pricing always matters operationally, cost per GB is not a differentiator between Cosmos DB and SQL Database because their pricing models are built around completely different units—request units (RU/s) versus DTUs or vCores—and actual cost depends on throughput, indexing, and read/write patterns. The same GB can be cheap or expensive depending on workload, so it's not a fixed factor that helps you choose between the two. You would instead analyze total cost of ownership for a specific use case.
- ✗
Maximum data size
Why it's wrong here
Neither Azure SQL Database nor Cosmos DB imposes a practically limiting maximum data size that would drive the choice; SQL Database scale tiers and Hyperscale go into the multi-terabyte range, and Cosmos DB automatically partitions your data with no need to shard manually. Both services scale out horizontally on the platform side, so max data size is a capacity-planning concern rather than a fundamental architecture decision. The far more important question is whether the data model and access patterns fit NoSQL or relational.
- ✓
Consistency model requirements
Why this is correct
Cosmos DB exposes five defined consistency levels—strong, bounded staleness, session, consistent prefix, and eventual—so you can tailor the trade-off between consistency, availability, and latency per request. SQL Database always gives you strong consistency with ACID transactions and SSI isolation, so you cannot relax consistency to reduce latency. When your application can tolerate weaker consistency for lower latency, this difference is a major decision factor.
Go deeper
Related to this question
Learn chapter
Key-Value Stores and In-Memory Caching
Key term
Azure SQL Database
Azure SQL Database is a fully managed relational database-as-a-service (DBaaS) in Microsoft Azure, based on the SQL Server engine, that handles scaling, backups, patching, and high availability automatically.
Key term
Schema
A schema is a blueprint or logical structure that defines how data is organized, stored, and accessed in a database or information system.
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