AZ-305 Design infrastructure solutions Practice Question
A company is designing a serverless architecture for a real-time data processing pipeline. The pipeline ingests data from IoT devices, processes the data using Azure Functions, and stores the results in Azure Cosmos DB. The solution must scale automatically and minimize cold starts. Which Azure service should the company use to trigger the Azure Functions?
⚠ Common exam trap
A common mix-up: candidates confuse Azure Event Hubs (a streaming ingestion service) with Azure Event Grid (an event routing service), mistakenly choosing Event Hubs for real-time triggers when it is actually designed for high-throughput data capture and requires additional processing layers, not direct function invocation.
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 Grid
Azure Event Grid is the correct choice because it provides a fully managed event routing service that can trigger Azure Functions in near real-time with sub-second latency, minimizing cold starts through its push-based model and support for serverless event handlers. It is ideal for IoT data ingestion scenarios where each device event needs to trigger a function independently, and it scales automatically to handle high throughput without requiring polling or batching.
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 Grid
Why this is correct
Event Grid is a fully managed event routing service that delivers events to subscribers via push-based HTTP calls, enabling Azure Functions to execute with near-zero latency and minimal cold starts. It can ingest device telemetry events directly from IoT Hub and route them to a function trigger without any polling layer. This push model is purpose-built for real-time serverless processing, making it the correct choice for this architecture.
- ✗
Azure Queue Storage
Why it's wrong here
Azure Queue Storage is a message queuing service for asynchronous, decoupled workloads; a function triggered by a queue polls for messages on a timed interval (by default every 16 seconds), which introduces inherent latency. It is not a push-based event broker, so it cannot deliver sub-second, real-time triggers to a serverless function. For a real-time architecture, using Queue Storage would create unnecessary delay and does not fit the event-driven pattern.
- ✗
Azure Event Hubs
Why it's wrong here
Event Hubs is a highly scalable streaming ingestion service optimized for millions of events per second, with consumers that process batches from partitions. While Azure Functions can be triggered by Event Hubs, the trigger is batch-oriented and uses checkpointing, and cold starts are more likely because the runtime scales with traffic spikes. It is designed for big-data analytics pipelines, not for delivering individual IoT events to a function with low latency.
- ✗
Azure Service Bus
Why it's wrong here
Service Bus is a broker-based enterprise messaging service that provides queues, topics, sessions, and transactions, with protocols such as AMQP and pull-based delivery. Its emphasis on ordered, durable, and trackable message handling adds overhead that is unsuitable for high-frequency IoT telemetry requiring immediate reaction. For real-time ingestion, Service Bus would force a polling or receive loop and is architecturally mismatched with a push-based serverless event architecture.
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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