AZ-305 Design data storage solutions Practice Question
A company ingests millions of IoT events per second from sensors around the world. Each event is a JSON message with timestamp, device ID, and readings. They need to support real-time analytics dashboards and also store all raw data for long-term historical analysis. They want to minimize operational overhead. Which Azure data storage solution should they recommend?
⚠ Common exam trap
Watch out — candidates often confuse Azure Data Lake Storage as a complete solution for both ingestion and storage, overlooking the need for a dedicated event ingestion service like Event Hubs to handle high-throughput streaming data before persisting it to the lake.
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 Event Hubs with Capture to Azure Data Lake Storage.
Azure Event Hubs is designed for high-throughput data ingestion, capable of handling millions of events per second. By enabling the Capture feature, data is automatically and durably persisted to Azure Data Lake Storage in Avro format, providing a serverless, low-latency pipeline for real-time dashboards while storing raw data for long-term analytics. This minimizes operational overhead by eliminating the need to manage separate ingestion and storage infrastructure.
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 Lake Storage Gen2 for all data.
Why it's wrong here
Data Lake Storage is a great store for historical data but does not provide real-time stream ingestion. You would still need an ingestion service. Additionally, storing every event directly to Data Lake without batching could be inefficient.
- ✓
Azure Event Hubs with Capture to Azure Data Lake Storage.
Why this is correct
Event Hubs can handle millions of events per second. The Capture feature automatically writes ingested events to Data Lake Storage in Avro format (or JSON). For real-time dashboards, you can use Stream Analytics to query the Event Hubs stream. This provides a seamless, low-operational-overhead solution.
- ✗
Azure Cosmos DB for both real-time and historical data.
Why it's wrong here
Cosmos DB can support real-time queries but at high write throughput it becomes very expensive. Storing billions of raw events in Cosmos DB for long-term is not cost-effective and does not utilize its strengths as a NoSQL document database.
- ✗
Azure Time Series Insights (TSI) Standard.
Why it's wrong here
TSI is purpose-built for IoT time-series data and can handle high cardinality. However, it is not ideal for storing raw JSON events long-term at massive scale, and the warm/cold store costs may be higher than Data Lake Storage. Also, TSI does not provide the flexibility of using other analytics tools on the data.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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