DP-203 Design and implement data storage Practice Question
You are designing a data storage solution for a retail company that expects high volumes of small, time-series sensor data from thousands of IoT devices. The data must be stored cost-effectively and queried by time range with low latency. Which Azure data store should you recommend?
⚠ Common exam trap
It's easy for candidates to choose Azure Blob Storage with Data Lake Storage Gen2 because it is cheap for storage, but they overlook that it lacks native query capabilities for low-latency time-series queries, requiring additional compute layers.
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 optimized for high-volume, time-series data ingestion and low-latency queries over time ranges. It uses a columnar storage engine and automatic indexing, making it cost-effective for sensor data from thousands of IoT devices.
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 with analytical store
Why it's wrong here
Cosmos DB is optimized for low-latency transactions, not high-throughput time-series analytics.
- ✗
Azure SQL Database with columnstore indexes
Why it's wrong here
SQL Database is not cost-effective for massive IoT data and lacks native time-series partitioning.
- ✗
Azure Blob Storage with Azure Data Lake Storage Gen2
Why it's wrong here
Blob Storage lacks native time-series querying and would require external compute, increasing latency and cost.
- ✓
Azure Data Explorer (ADX)
Why this is correct
ADX is designed for high-speed ingestion and real-time analytics on time-series data with built-in optimization.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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