DP-203 Design and implement data storage Practice Question
You are designing a data storage solution for real-time analytics on IoT telemetry. The system must ingest 10,000 events per second and support sub-second query latency. Which Azure data store should you use?
⚠ Common exam trap
It's easy for candidates to confuse Azure Cosmos DB's analytical store with a real-time analytics solution, but it is designed for hybrid transactional/analytical processing (HTAP) on operational data, not for high-velocity streaming telemetry analytics where ADX excels.
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 (ADX).
Azure Data Explorer (ADX) is purpose-built for high-velocity telemetry and log analytics, ingesting 10,000+ events per second with sub-second query latency via its columnar storage and distributed query engine. It supports real-time analytics on streaming IoT data without requiring pre-defined schemas or indexing, making it the optimal choice for this scenario.
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 Table Storage.
Why it's wrong here
Azure Table Storage is a key-value NoSQL store with no native time-series ingestion pipeline or sub-second analytical query engine; it suits cheap schema-less lookups, not 10,000 events/second telemetry analytics. Azure Data Explorer handles that throughput and latency.
- ✗
Azure SQL Database with in-memory OLTP.
Why it's wrong here
In-memory OLTP accelerates transactional processing within a relational engine, but Azure SQL Database lacks the partitioned ingestion pipeline and global distribution needed for 10,000 events per second. It is the right choice for low-latency OLTP applications, not telemetry analytics.
- ✗
Azure Cosmos DB with analytical store.
Why it's wrong here
The analytical store is columnar and optimised for large-scale analytical queries over synced operational data, introducing latency unsuited to sub-second reads on live telemetry. It is correct for near-real-time reporting over Cosmos DB data, not high-rate ingestion with immediate query.
- ✓
Azure Data Explorer (ADX).
Why this is correct
Azure Data Explorer ingests telemetry at high throughput and indexes it for sub-second queries, satisfying both the 10,000 events per second and low-latency constraints. Its columnar store and Kusto engine suit time-series IoT analytics, unlike row-store or batch-oriented alternatives.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-203 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-203 exam.