AI-102 Plan and manage an Azure AI solution Practice Question
You are deploying an Azure AI solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must process documents in near real-time and must be able to handle sudden spikes in volume. You need to design the architecture to meet these requirements while minimizing cost. What should you use?
⚠ Common exam trap
The trap here is assuming that Logic Apps are always cheaper, but polling and scaling limits can increase cost and delay.
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 Functions with a blob trigger that calls the Document Intelligence API and uses a consumption plan.
For near real-time processing with sudden spikes and minimal cost, a serverless approach with Azure Functions on a consumption plan is best. The blob trigger ensures immediate processing when documents are uploaded, and the consumption plan scales automatically and charges only for execution time. Other options introduce latency, require infrastructure management, or do not scale to zero.
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 Logic Apps with a recurrence trigger that polls the blob container every minute.
Why it's wrong here
A recurrence trigger introduces up to a one-minute delay, which may not meet near real-time requirements. Logic Apps also have scaling limits and can be more expensive for high-volume processing. Polling is less efficient than event-driven triggers. While Logic Apps are low-code, they may not handle sudden spikes as cost-effectively as serverless functions.
- ✓
Azure Functions with a blob trigger that calls the Document Intelligence API and uses a consumption plan.
Why this is correct
Azure Functions with a blob trigger can process documents as they are uploaded, providing near real-time processing. The consumption plan automatically scales out during spikes and scales in when idle, minimizing cost. This serverless approach is ideal for unpredictable workloads and reduces infrastructure management.
- ✗
Azure Kubernetes Service (AKS) with a horizontal pod autoscaler that processes documents from a queue.
Why it's wrong here
AKS provides scalability but requires managing the cluster, nodes, and networking, which adds cost and complexity. It does not scale to zero by default, so you incur costs even when idle. While it can handle spikes, it is not the most cost-effective for sporadic workloads. Serverless functions are better suited for minimizing cost with variable volume.
- ✗
Azure Batch with a pool of virtual machines that processes documents from a queue.
Why it's wrong here
Azure Batch is designed for large-scale parallel processing but requires managing a pool of VMs, which increases cost and administrative overhead. It does not automatically scale to zero, so you pay for idle capacity. For near real-time and cost minimization, a serverless approach is more appropriate. Batch is better for long-running, high-throughput jobs.
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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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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