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
You are designing a solution to store IoT device telemetry data. Each message is a small JSON payload (1-2 KB). The data is written once and read frequently for real-time dashboards. Which Azure data store should you use?
⚠ Common exam trap
A common mix-up: candidates choose Azure Blob Storage because they associate 'JSON payloads' with 'files,' overlooking that Blob Storage lacks the low-latency query and indexing capabilities required for real-time dashboards, while Cosmos DB is purpose-built for such operational workloads.
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 correct choice because it is a globally distributed, multi-model database service that offers single-digit millisecond read and write latencies at any scale, making it ideal for real-time dashboards consuming IoT telemetry. Its support for JSON documents natively aligns with the small JSON payloads, and its ability to handle high-throughput writes (once) and low-latency reads (frequently) without schema management fits the workload perfectly.
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 SQL Database
Why it's wrong here
Azure SQL Database is a relational system that enforces a strict schema and ACID transactions. Managing rapidly evolving IoT telemetry payloads would require frequent ALTER TABLE migrations and breaking changes. It also does not scale out for high-volume, small write operations as easily as Cosmos DB, and connection management for frequent device messages adds overhead. Therefore SQL Database is wrong for this low-latency, schema-flexible workload.
- ✓
Azure Cosmos DB
Why this is correct
Azure Cosmos DB is correct because it is a multi-model NoSQL database with native JSON document support, schema-agnostic ingestion, and horizontally scaled partitions. It provides single-digit-millisecond reads and high write throughput at any scale, which is ideal for high-frequency device telemetry. Time-series data can be partitioned by device ID or timestamp, and SQL-like queries are supported. This directly matches the requirement for low-latency reads of JSON telemetry.
- ✗
Azure Blob Storage
Why it's wrong here
Azure Blob Storage is an object store designed for large, immutable binary files such as images or archives, not for millions of small, frequent telemetry messages. Each write is a full object operation, so small writes are inefficient and expensive, and point reads require first listing or knowing a long blob path, adding latency. You could use blobs as a raw data lake for telemetry after aggregation, but they cannot serve frequent low-latency device reads. Thus it is wrong for this workload.
- ✗
Azure Table Storage
Why it's wrong here
Azure Table Storage is a NoSQL key-value store that lacks complex query capabilities: it only supports simple OData filters and requires partition keys for efficient access. Telemetry analysis often needs time-based or cross-device aggregations, which would force slow table scans or client-side joins. Cosmos DB, by contrast, indexes all fields and supports robust SQL-like queries with multiple partitioning techniques. Therefore Table Storage is less suited to this scenario.
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
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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Senior Network & Security Engineer · founder of Courseiva
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