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DP-900 Describe core data concepts Practice Question

A company needs to store semi-structured data from IoT devices, including temperature readings and device status. The data will be queried by time range and device ID. Which Azure data service is most cost-effective for this use case?

⚠ Common exam trap

A common mix-up: candidates choose Azure Cosmos DB for its NoSQL capabilities, overlooking the fact that Table Storage provides the same key-value functionality at a fraction of the cost for simple IoT 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 Table Storage

Azure Table Storage is a NoSQL key-value store that is optimized for storing large amounts of semi-structured data without requiring a fixed schema. It supports efficient queries by partition key (device ID) and row key (timestamp), making it ideal for time-series IoT data at a lower cost than other Azure data services.

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 Blob Storage

    Why it's wrong here

    Azure Blob Storage is optimized for storing massive amounts of unstructured binary data, such as images, videos, and log files, rather than small, queryable telemetry records. While you could package JSON documents as blobs, there is no native row-level query capability to efficiently filter by device ID or timestamp. Retrieval requires enumerating and deserializing blobs, making it impractical for high-frequency IoT data access compared to a NoSQL key-value service.

  • Azure Cosmos DB

    Why it's wrong here

    Azure Cosmos DB is a fully managed, globally distributed multi-model NoSQL database that offers rich querying, multiple consistency levels, and turnkey global distribution. However, these advanced capabilities come with significantly higher costs due to provisioned throughput (request units) and storage pricing, far exceeding the needs of simple IoT telemetry retrieval by device ID. For this scenario, the cost and operational complexity are unjustified, making Azure Table Storage a leaner and more cost-effective choice.

  • Azure SQL Database

    Why it's wrong here

    Azure SQL Database enforces a fixed relational schema, requiring every telemetry record to conform to predefined columns and data types. IoT device data is often semi-structured and evolving, and the volume of writes can be very high; a relational engine adds overhead for joins and transaction guarantees that are unnecessary for simple key-value point lookups. The associated licensing and compute costs also make it less economical than a purpose-built NoSQL store for this workload.

  • Azure Table Storage

    Why this is correct

    Azure Table Storage is a schemaless NoSQL key-value store that provides massive scalability and low-cost storage, with each entity accessible via a partition key and row key. It naturally fits IoT telemetry where device ID serves as the partition key and timestamp as the row key, enabling fast and simple point queries without requiring complex query languages. Being part of Azure Storage, it offers high availability, global redundancy options, and an inexpensive pay-per-storage model ideal for high-volume telemetry ingestion.

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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.