DP-900 Practice Question: Describe considerations for working with non-relational data on Azure
You need to store telemetry data from millions of devices. Each record includes a device ID, timestamp, and metric value. The data will be queried by device ID and time range. Which Azure data store is best suited for this scenario?
⚠ Common exam trap
DP-900 often tests whether candidates confuse transactional NoSQL stores (Cosmos DB) with analytical time-series stores (Azure Data Explorer), so the key is recognizing the query pattern (device ID + time range) and the scale (millions of devices).
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 Data Explorer
Azure Data Explorer (ADX) is a fully managed, high-performance analytics service optimized for ingesting and querying large volumes of telemetry and log data using Kusto Query Language (KQL). It is purpose-built for time-series and IoT scenarios where data is queried by device ID and time range, offering sub-second query performance over billions of records.
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 Data Explorer
Why this is correct
Azure Data Explorer (ADX) is a fully managed analytics database purpose-built for high-volume time-series and log data. Its columnar storage engine uses advanced indexing and compression to ingest millions of events per second while retaining interactive query performance via Kusto Query Language (KQL). KQL includes native time-series functions like bin(), summarize, and percentiles, enabling near-real-time telemetry analytics without external processing. This makes ADX the optimal choice for telemetry pipelines that demand fast ingestion, long-term retention, and complex temporal queries.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a relational database management system optimized for online transaction processing (OLTP) with row-based storage, which yields high overhead when ingesting and querying continuous telemetry streams. It lacks native time-series features such as automatic data retention policies, downsampling, or time-based partitioning, so you would need to manually implement partitioning and indexing to avoid performance degradation. While it can technically store telemetry data, its architecture struggles to scale cost-effectively to millions of devices generating high-cardinality events, making it a poor fit for this workload.
- ✗
Azure Storage Queues
Why it's wrong here
Azure Storage Queues is a message queuing service designed for asynchronous decoupling of application components, not for persistent storage or analytical querying. Messages are transient, have a maximum time-to-live of 7 days, and support a small payload size, so telemetry would be lost quickly and cannot be retained for historical analysis. Additionally, Storage Queues provides no query language, aggregation operators, or indexing, making it impossible to run analytical queries on the data. It could serve as a temporary ingestion buffer, but it is not a storage solution for telemetry.
- ✗
Azure Cosmos DB
Why it's wrong here
Azure Cosmos DB is a multi-model NoSQL database with low-latency access and global distribution, but it incurs high request-unit (RU) costs when ingesting high-throughput telemetry because every write is indexed on multiple paths by default. Its document-oriented storage is not columnar, and it lacks time-series-specific optimizations like time-based partitioning, retention policies, and specialized temporal query functions (e.g., downsampling, interpolations). Although Cosmos DB can store IoT data, its per-operation cost and lack of analytical performance make it significantly more expensive and less suitable than Azure Data Explorer for large-scale time-series analytics.
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Related to this question
Learn chapter
Key-Value Stores and In-Memory Caching
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.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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