Describe considerations for working with non-relational data on Azure →easyMultiple ChoiceObjective-mapped
DP-900 Practice Question: Describe considerations for working with non-relational data on Azure
A retail company stores product catalog data as JSON documents. Each product has a different set of attributes depending on its category (e.g., electronics have 'voltage', clothing has 'size'). The application needs to query products by category and price range efficiently. Which Azure data store is most appropriate for this workload?
⚠ Common exam trap
A common mix-up: candidates choose Azure SQL Database because they assume all structured data requires a relational store, overlooking the fact that JSON documents with varying schemas are better served by a NoSQL document database like Cosmos DB.
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
Azure Cosmos DB is the most appropriate choice because it natively supports JSON documents with flexible schemas, enabling each product to have a different set of attributes per category. Its indexing policies can be configured to efficiently support queries filtering by category and price range, and it offers low-latency, high-throughput access ideal for retail catalog workloads.
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
Why this is correct
Azure Cosmos DB is a multi-model NoSQL database with native JSON support and schema-agnostic automatic indexing, allowing queries on any attribute such as category or price without predefined schema. Its low-latency index on every property makes it ideal for product catalogs where different items have varying attributes and customers filter by arbitrary combinations. Cosmos DB also offers predictable throughput scaling, ensuring consistent query performance as catalog size grows.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a relational engine that requires a fixed, normalized schema; although it can parse JSON with functions like JSON_VALUE and OPENJSON, it does not automatically index nested JSON attributes. Querying variable product attributes from JSON in SQL needs careful schema design, computed columns, or full-text indexing, making it cumbersome for catalogs with many dissimilar product types. It also enforces ACID transactions that are unnecessary for read-heavy catalog lookups, adding overhead without benefit.
- ✗
Azure Blob Storage
Why it's wrong here
Azure Blob Storage is an object store intended for unstructured binary or text data, such as images, videos, and backup files, and it treats each JSON document as an opaque blob with no indexing of its contents. To query fields inside JSON in Blob Storage, you would need to build a separate search service like Azure AI Search or run a compute layer to load and parse every blob, which is neither native nor efficient. Its strengths lie in cheap, scalable storage, not in interactive multi-attribute queries.
When this WOULD be correct
A question where the workload involves storing and serving large media files (e.g., product images, videos) with infrequent access, and querying is not required or is handled by a separate search service like Azure Cognitive Search.
- ✗
Azure Table Storage
Why it's wrong here
Azure Table Storage is a NoSQL key-value store where data is retrieved via partition key and row key, and it lacks secondary indexes or server-side query support for arbitrary attributes. Filtering by both category and price range would force a full partition scan and client-side evaluation, which becomes slow and expensive as catalog data grows. Its simple API is designed for high-volume storage of entities with homogeneous schemas, not for dynamic JSON documents with rich query patterns.
When this WOULD be correct
A question where the workload requires storing large volumes of structured, non-relational data (e.g., IoT device telemetry) with simple key-based lookups and no need for complex queries or indexing on multiple properties. The data is flat and accessed primarily by partition key and row key.
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.
✓Azure Cosmos DBCorrect answer▾
Why this is correct
Azure Cosmos DB is a multi-model NoSQL database with native JSON support and schema-agnostic automatic indexing, allowing queries on any attribute such as category or price without predefined schema. Its low-latency index on every property makes it ideal for product catalogs where different items have varying attributes and customers filter by arbitrary combinations. Cosmos DB also offers predictable throughput scaling, ensuring consistent query performance as catalog size grows.
✗Azure Blob StorageWrong answer — click to see why▾
Why this is wrong here
Azure Blob Storage is optimized for unstructured binary or text data (e.g., images, backups) and lacks native querying for JSON documents by attributes like category and price range, requiring costly full scans or external indexing.
★ When this WOULD be the correct answer
A question where the workload involves storing and serving large media files (e.g., product images, videos) with infrequent access, and querying is not required or is handled by a separate search service like Azure Cognitive Search.
Why candidates choose this
Candidates may think JSON documents are just text files that can be stored in Blob Storage, overlooking the need for efficient attribute-based queries that Blob Storage cannot natively support.
✗Azure Table StorageWrong answer — click to see why▾
Why this is wrong here
Azure Table Storage is a NoSQL key-value store that does not support native JSON document storage or complex queries on nested attributes like price range and category. It lacks indexing on arbitrary properties, making efficient queries on varying product attributes impossible.
★ When this WOULD be the correct answer
A question where the workload requires storing large volumes of structured, non-relational data (e.g., IoT device telemetry) with simple key-based lookups and no need for complex queries or indexing on multiple properties. The data is flat and accessed primarily by partition key and row key.
Why candidates choose this
Candidates may confuse Azure Table Storage with a document database because it is a NoSQL store, but they overlook its lack of native JSON support and limited query capabilities for nested or varying attributes.
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
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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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.