BenchMark Quizzes by MDFT Pro

Select Cosmos DB API for Geospatial Data

You work as a Mobile Application Developer for MDFT Pro, a well-known training agency that delivers certification courses to students worldwide. Mark, the Product Innovation Manager, has asked you to develop a mobile app that helps students find suitable study locations near their current position. The app will use an API that stores geospatial data in Azure Cosmos DB, including information about partner study centers, coffee shops with quiet study areas, libraries, and co-working spaces where students can study for their certification exams. The app needs to find locations within a particular radius of the user’s current GPS coordinates, retrieve related information including amenities (WiFi availability, quiet zones, power outlets), opening hours, and calculate the optimal walking or driving route from the student’s current location to the selected study location. The API must efficiently handle geospatial queries with support for location-based operations and distance calculations.

Which Azure Cosmos DB API should you use for implementing this geospatial functionality?

Choose the correct answer from the options below.

Explanations for each answer:

MongoDB is correct. Azure Cosmos DB for MongoDB supports native geospatial operations with GeoJSON data, including location-based queries like finding nearby locations, calculating distances, and determining optimal routes. The MongoDB API provides geospatial operators like $near, $geoWithin, and $geoIntersects that are ideal for restaurant finder applications requiring proximity searches and route calculations.
Gremlin is incorrect. Gremlin API is designed for graph database scenarios where you need to model relationships between entities (like social networks or recommendation systems). While powerful for traversing connections, it's not optimized for geospatial queries involving coordinates, proximity searches, or route calculations.
Cassandra is incorrect. Cassandra API is optimized for wide-column data storage with high write throughput and low latency reads. While it can store location data as regular columns, it lacks native geospatial indexing and query operators needed for efficient proximity searches and route calculations.
Core (SQL) is incorrect. While the Core (SQL) API does support geospatial data through GeoJSON and provides functions like ST_DISTANCE and ST_WITHIN, the MongoDB API offers more comprehensive native geospatial support with additional operators and is better suited for applications primarily focused on location-based queries.

Learn more about Geospatial Queries:

MongoDB Geospatial Support

Practice Exam

Select Cosmos DB API for Geospatial Data

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