AZ-305 Design data storage solutions Practice Question
A company ingests millions of IoT sensor data points per second. They need a fully managed analytics service optimized for time-series data that can ingest high-velocity data, perform real-time analytics, and store data for historical analysis. The solution must integrate with Azure Stream Analytics for stream processing. Which Azure data service should they choose?
⚠ Common exam trap
Candidates often confuse Azure Data Explorer with Azure Cosmos DB or Azure SQL Database because they all support time-series data, but only ADX is purpose-built for high-velocity ingestion and real-time analytics with native Stream Analytics integration.
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 the correct choice because it is a fully managed, high-performance analytics service optimized for time-series and log data. It can ingest millions of IoT sensor data points per second, perform real-time analytics with sub-second query latency, and store data for historical analysis. ADX natively integrates with Azure Stream Analytics for stream processing, making it ideal 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 Cosmos DB
Why it's wrong here
Azure Cosmos DB is a multi-model NoSQL database that provides extremely low latency reads and writes at global scale, but it is not optimized for the sustained, high-velocity ingestion typical in IoT workloads. In Cosmos DB, every write consumes request units (RUs), and its document-oriented row-store architecture is ill-suited for the columnar, compression-heavy storage that makes massive time-series analytics efficient. While Cosmos DB can integrate with Azure Data Explorer to serve time-series queries, using it as the primary ingestion and analytics engine for millions of events per second would require an enormous RU allocation and still lack native time-series functions.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a relational database management system designed primarily for online transaction processing (OLTP) with strong consistency and ACID guarantees. Its row-based storage and logging overhead impose a significant bottleneck under millions of writes per second, especially when every insert requires index maintenance and transaction log flushing. Even with clustered columnstore indexes, Azure SQL Database is not purpose-built for the append-only, scan-heavy query patterns of real-time time-series analytics, and you would need to shard or partition extensively to approach ADX's throughput.
- ✓
Azure Data Explorer (ADX)
Why this is correct
Azure Data Explorer (ADX) is a big data analytics service specifically engineered for time-series and log data, combining a columnar storage engine with a distributed, scale-out architecture. It can ingest millions of events per second from Azure Stream Analytics, IoT Hub, or Event Hubs, and its Kusto Query Language (KQL) provides native time-series functions such as bin(), make-series, and series_decompose for real-time aggregation, anomaly detection, and forecasting. The engine uses automatic indexing, caching, and data compression to deliver rapid query responses over billions of records, making it the correct choice for this IoT scenario.
- ✗
Azure Blob Storage
Why it's wrong here
Azure Blob Storage is an object-based, unstructured storage service designed for cheap, durable storage at massive scale, but it has no native analytics or query engine. To analyze the streaming IoT data stored in blobs, you would need to interactively query it via additional compute services like Azure Synapse Analytics or Azure Databricks, which adds latency and complexity that defeats real-time analytics. Moreover, blob ingestion is inherently batch-oriented—using triggers or event subscriptions to process new blobs—and cannot sustain the sub-second, continuous query performance that ADX offers for time-series workloads.
Go deeper
Related to this question
About these practice questions
One of 795 original AZ-305 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AZ-305 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 AZ-305 exam.