Describe considerations for working with non-relational data on Azure →mediumMultiple ChoiceObjective-mapped
Azure Cosmos DB Global Distribution with Multi-Region Writes
A mobile game company stores player profiles and game state in Azure Cosmos DB. Each document contains playerId, level, score, inventory (an array of items), and lastLogin. The application requires fast point reads by playerId, queries to find all players within a specific score range, and global distribution with multi-region writes for low latency worldwide. They also want to use a familiar SQL-like query language. Which Azure Cosmos DB API should they choose?
Quick Answer
The Core (SQL) API is the correct choice because it natively supports SQL-like queries for fast point reads by playerId and range queries on score, while also enabling Azure Cosmos DB global distribution with multi-region writes for low latency worldwide. This API directly handles the document model with arrays like inventory, making it ideal for player profiles and game state. On the DP-900 exam, this scenario tests your understanding that the Core API is the default, multi-model option for SQL-based querying and global distribution—a common trap is confusing it with the Table API (which lacks SQL querying) or MongoDB API (which uses a different syntax). Remember: if the requirement says "SQL-like queries" and "multi-region writes," think Core API. A simple memory tip: "SQL for queries, Core for global writes."
⚠ Common exam trap
Many exam-takers confuse the MongoDB API's use of a familiar query language (MongoDB's own) with SQL-like syntax, or assume that any NoSQL API can handle range queries equally, but the Core (SQL) API is the only one that provides native SQL-like querying with automatic indexing for such patterns.
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
✓
Core (SQL) API
The Core (SQL) API is the correct choice because it provides native support for SQL-like queries, enabling the required point reads by playerId and range queries on score. It also offers multi-region writes for global distribution with low latency, which aligns with the application's need for worldwide player access. The document model with arrays (inventory) is directly supported, making it ideal for storing player profiles and game state.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Core (SQL) API
Why this is correct
Correct. The Core (SQL) API provides a SQL-like query language, supports point reads and range queries, and enables multi-region writes for global distribution.
- ✗
MongoDB API
Why it's wrong here
The MongoDB API uses MongoDB's query language (not SQL) and supports multi-region writes, but the team prefers a SQL-like syntax, so it is not the best fit.
When this WOULD be correct
A question where the application already uses MongoDB drivers and requires compatibility with existing MongoDB tooling, or where the team is more familiar with MongoDB's query language and does not need SQL-like syntax. For example: 'A company migrating an existing MongoDB application to Azure Cosmos DB wants minimal code changes.'
- ✗
Cassandra API
Why it's wrong here
The Cassandra API uses CQL, which resembles SQL, but the primary querying pattern is by partition key. Range queries on non-partition key fields like score may require careful modelling and are less efficient than the Core (SQL) API for arbitrary queries.
When this WOULD be correct
A question where the application requires high-throughput writes, a wide-column data model, and eventual consistency, with no need for SQL-like queries or multi-region writes. For example: 'A telemetry system ingests millions of events per second from IoT devices, requiring a schema-flexible, horizontally scalable database with strong consistency on writes.'
- ✗
Gremlin API
Why it's wrong here
The Gremlin API is designed for graph data and graph traversals, not for document-based queries with SQL-like syntax. It would not be appropriate for player profiles and scores.
When this WOULD be correct
A question where the application needs to model and query complex relationships, such as a social network where users are connected to friends, and you need to traverse paths (e.g., find friends of friends) or analyze graph patterns. The Gremlin API would be correct for such graph workloads.
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.
✓Core (SQL) APICorrect answer▾
Why this is correct
Correct. The Core (SQL) API provides a SQL-like query language, supports point reads and range queries, and enables multi-region writes for global distribution.
✗MongoDB APIWrong answer — click to see why▾
Why this is wrong here
The MongoDB API does not support multi-region writes with a SQL-like query language; it uses MongoDB's query syntax. The question requires SQL-like queries and global distribution with multi-region writes, which the Core (SQL) API provides natively.
★ When this WOULD be the correct answer
A question where the application already uses MongoDB drivers and requires compatibility with existing MongoDB tooling, or where the team is more familiar with MongoDB's query language and does not need SQL-like syntax. For example: 'A company migrating an existing MongoDB application to Azure Cosmos DB wants minimal code changes.'
Why candidates choose this
Candidates may confuse the MongoDB API's support for documents and JSON with the ability to use SQL-like queries, or they may think 'familiar query language' refers to MongoDB's query language rather than SQL.
✗Cassandra APIWrong answer — click to see why▾
Why this is wrong here
The Cassandra API does not support SQL-like queries or multi-region writes with low latency; it uses CQL (Cassandra Query Language) and is optimized for high-throughput writes but not for global distribution with multi-region writes.
★ When this WOULD be the correct answer
A question where the application requires high-throughput writes, a wide-column data model, and eventual consistency, with no need for SQL-like queries or multi-region writes. For example: 'A telemetry system ingests millions of events per second from IoT devices, requiring a schema-flexible, horizontally scalable database with strong consistency on writes.'
Why candidates choose this
Candidates may confuse Cosmos DB's Cassandra API with the general Cassandra database, thinking it supports SQL-like queries or global distribution, but the Cassandra API is limited to CQL and does not offer multi-region writes.
✗Gremlin APIWrong answer — click to see why▾
Why this is wrong here
The Gremlin API is designed for graph databases and querying relationships between entities, not for document-based queries like point reads by playerId or score range queries. It does not support SQL-like query language.
★ When this WOULD be the correct answer
A question where the application needs to model and query complex relationships, such as a social network where users are connected to friends, and you need to traverse paths (e.g., find friends of friends) or analyze graph patterns. The Gremlin API would be correct for such graph workloads.
Why candidates choose this
Candidates may confuse 'global distribution' and 'multi-region writes' with graph databases, or they might think Gremlin is a general-purpose API because it supports complex queries, but it is specialized for graph data.
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?”
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Azure Cosmos DB
Azure Cosmos DB is a fully managed, globally distributed NoSQL database service that offers fast reads and writes anywhere in the world with automatic scaling and multiple consistency models.
Key term
Cosmos DB API
Cosmos DB API is a collection of interfaces that lets applications interact with Azure Cosmos DB, a globally distributed NoSQL database, using different data models and query languages.
About these practice questions
This DP-900 question is part of Courseiva's 820-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more way this is tested on DP-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A social media company stores user-generated posts as JSON documents. Each post contains fields such as postId, userId, timestamp, and content. The application needs to query posts by userId and timestamp ranges with low latency, and also perform SQL-like queries across all posts. The data volume is growing rapidly and must scale globally. Which Azure data store should the company use?
medium- A.A) Azure Table Storage
- ✓ B.B) Azure Cosmos DB SQL API
- C.C) Azure Blob Storage
- D.D) Azure Cache for Redis
Why B: Azure Cosmos DB SQL API is the correct choice because it provides native support for querying JSON documents with low-latency, including indexed queries on fields like userId and timestamp. Its global distribution capability ensures data can be replicated across multiple Azure regions for low-latency access worldwide, while its SQL API allows SQL-like queries across all posts, meeting both requirements.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-900 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-900 exam.