Describe considerations for working with non-relational data on Azure →mediumMultiple ChoiceObjective-mapped
DP-900 Practice Question: Describe considerations for working with non-relational data on Azure
A retail company is designing a product catalog for its e-commerce website. Each product has a unique ProductID, a name, a price, and a variable number of attributes (e.g., size, color, weight) that differ across product categories. The application requires ability to read a product's details by ProductID with single-digit millisecond latency from any Azure region globally. The schema must be flexible to accommodate new attributes without schema changes. Which Azure data store should the company choose?
⚠ Common exam trap
It's easy for candidates to confuse Azure Table Storage's flexible schema and global distribution with Cosmos DB's performance guarantees, overlooking the specific single-digit millisecond latency requirement that only Cosmos DB can consistently meet across all regions.
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
✓
Azure Cosmos DB using the NoSQL API
Azure Cosmos DB with the NoSQL API is correct because it provides a fully managed, globally distributed NoSQL database that supports flexible schemas (allowing variable product attributes without schema changes) and guarantees single-digit millisecond read latency at any scale from any Azure region via its multi-region write and read replicas. The unique ProductID serves as a natural partition key, enabling efficient point reads with consistent low latency.
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 Cosmos DB using the NoSQL API
Why this is correct
Azure Cosmos DB using the NoSQL API is the correct choice because it provides schema-agnostic document storage that adapts to varying product attributes without migrations. It also guarantees single-digit millisecond latency for point reads (under 10 ms) at any scale, supported by a 99.999% availability SLA. Its turnkey global distribution allows replicas across Azure regions, ensuring low-latency access for e-commerce customers worldwide, and the SQL-like query engine supports rich filtering and projection over flexible JSON documents.
- ✗
Azure Table Storage
Why it's wrong here
Azure Table Storage is a schema-less NoSQL key-value store, but it lacks the global distribution and low-latency guarantees that Azure Cosmos DB offers. It does not provide automatic replication across regions with tunable consistency, nor does it offer a read latency SLA of under 10 ms for point reads. While it is inexpensive for simple key-value scenarios, it is not designed for complex product catalog queries, secondary indexes, or stored procedures, making it unsuitable for a scalable e-commerce catalog.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a relational database that requires a predefined schema, meaning each product type would need the same rigid columns or many nullable columns and JOIN-based attribute tables. This restricts attribute flexibility, making it cumbersome to add new product-specific fields or handle heterogeneous product categories without time-consuming ALTER TABLE operations. It also does not natively provide global distribution with a single write region and multi-region reads with the same turnkey deployment model as Cosmos DB, and its read latency is not guaranteed to be under 10 ms for point lookups.
- ✗
Azure Blob Storage
Why it's wrong here
Azure Blob Storage is object storage tuned for storing large unstructured binary data such as images, videos, and documents, not for serving individual structured product items. It lacks built-in indexing, query semantics, and the ability to perform point reads with single-digit millisecond latency; retrieving an item requires a full object download with HTTP overhead. While it could store serialized JSON files, there is no native database capability like filtering, secondary indexes, or transactional consistency, making it completely inappropriate for a low-latency product catalog.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
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.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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