AZ-305 Design infrastructure solutions Practice Question
A global logistics company is designing a solution that ingests telemetry data from thousands of IoT devices. The data must be processed in near real-time to detect anomalies and trigger alerts. The company wants to use a serverless, event-driven architecture that can scale automatically and minimize operational overhead. Which Azure service should the solutions architect use for the stream processing component?
⚠ Common exam trap
The trap here is assuming that Azure Functions is always the best serverless choice, but for complex stream processing with built-in operators, Stream Analytics is more appropriate.
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 Stream Analytics
Azure Stream Analytics is a serverless stream processing service that excels at real-time analytics on streaming data. It can ingest from IoT Hub or Event Hubs, apply SQL-like queries for anomaly detection, and output alerts to various destinations. It scales automatically and eliminates the need to manage infrastructure, aligning with the company's requirements for a serverless, event-driven architecture.
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 Event Hubs with Capture enabled
Why it's wrong here
Event Hubs with Capture automatically saves the stream to Azure Blob Storage or Data Lake Storage, but it does not provide a processing engine. It is a data ingestion and buffering service. While it can be part of the pipeline, it does not perform the real-time anomaly detection or alerting required. You would still need a separate stream processing service.
- ✗
Azure Functions with an Event Hub trigger
Why it's wrong here
Azure Functions is a serverless compute service that can process events from Event Hubs, but it is not a dedicated stream processing engine. For complex stream processing with windowing, temporal joins, and anomaly detection, you would need to write custom code, which increases operational overhead. Stream Analytics provides built-in operators and is more suitable for this scenario.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is a fully managed, serverless stream processing service that can analyze and process high volumes of streaming data from sources like IoT Hub or Event Hubs. It supports SQL-like queries for real-time analytics, anomaly detection, and can trigger alerts by outputting to services like Azure Functions or Logic Apps. It scales automatically and requires no infrastructure management.
- ✗
Azure Logic Apps with an IoT Hub connector
Why it's wrong here
Logic Apps is a serverless workflow engine designed for integration and orchestration, not for high-throughput stream processing. It is not optimized for near real-time analytics on thousands of events per second and would likely be cost-prohibitive and too slow. It lacks the built-in stream analytics capabilities needed for anomaly detection.
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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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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.