AZ-305 Design infrastructure solutions Practice Question
A manufacturing company is designing an IoT solution to monitor equipment in real-time. Thousands of sensors send telemetry data every second. The data must be ingested, processed, and stored for analysis. The solution must handle high throughput and provide low-latency analytics. Additionally, the company wants to use Azure Machine Learning to predict equipment failures based on historical data. You need to design a data pipeline that meets these requirements. What should you include in the design?
⚠ Common exam trap
Many exam-takers confuse Azure IoT Hub with Azure Event Hubs, overlooking IoT Hub's superior device management and security features for sensor fleets, or they mistakenly pair Azure Databricks with Event Hubs assuming it provides lower latency than Stream Analytics, when in fact Stream Analytics is purpose-built for sub-second stream processing.
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
✓
Use Azure IoT Hub to ingest data, Azure Stream Analytics for real-time processing, and Azure Blob Storage for long-term storage.
Azure IoT Hub is designed for secure, high-throughput ingestion from millions of IoT devices, Azure Stream Analytics provides low-latency, real-time processing using SQL-like queries, and Azure Blob Storage offers cost-effective, durable long-term storage for historical data. This combination directly meets the requirements for real-time monitoring and supports downstream Azure Machine Learning workloads by storing historical telemetry in a format easily accessible for model training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Azure IoT Hub to ingest data, Azure Stream Analytics for real-time processing, and Azure Blob Storage for long-term storage.
Why this is correct
Azure IoT Hub is the ideal cloud gateway for IoT because it provides per-device identity, secure authentication, and built-in message routing to downstream services. Stream Analytics then handles real-time, SQL-like queries over high-throughput telemetry without requiring custom code, while Blob Storage offers inexpensive, tiered long-term retention. Together they form a scalable hot/warm/cold pipeline that is standard for IoT telemetry workloads.
- ✗
Use Azure IoT Hub to ingest data, Azure Cosmos DB for storage, and Azure Functions for processing.
Why it's wrong here
Azure Cosmos DB is optimized for low-latency NoSQL queries and transactional workloads, but its request-unit (RU) throughput cost and per-gigabyte price make it prohibitively expensive for storing massive streams of raw device telemetry. Azure Functions is an event-driven, concurrency-bound compute service, so it cannot reliably replace a continuous streaming engine like Stream Analytics for sustained high-volume ingestion. This pairing sacrifices both cost efficiency and throughput for no architectural benefit.
- ✗
Use Azure IoT Hub to ingest data, Azure Data Lake Storage for storage, and Azure Stream Analytics for processing.
Why it's wrong here
Azure Data Lake Storage (Gen2) is primarily a high-capacity analytical data lake service, and while Stream Analytics can output to it, the combination is better suited for big-data analytics than for simple IoT telemetry archival. Choosing Data Lake Storage over Blob Storage adds hierarchical-namespace and analytics-oriented features that are unnecessary for long-term raw telemetry retention, increasing cost and complexity. Blob Storage provides the same durable, tiered cold storage at a lower price point, making it the more appropriate choice for this scenario.
- ✗
Use Azure Event Hubs to ingest data, Azure Databricks for processing, and Azure Blob Storage for storage.
Why it's wrong here
Azure Event Hubs can ingest millions of events per second, but it lacks IoT-specific functionality such as a device identity registry, device twin properties, and direct method calls that IoT Hub provides. Azure Databricks is an Apache Spark-based analytics platform that requires cluster management, complex configuration, and significant compute cost, making it overkill for simple low-latency stream processing of telemetry. The standard architecture pairs IoT Hub with Stream Analytics, not Event Hubs and Databricks.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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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.