AI-102 Implement generative AI solutions Practice Question
You are building a generative AI application that must process large volumes of PDF documents and generate summaries using Azure OpenAI. The solution must be cost-effective and handle variable workloads. Which architecture should you recommend?
⚠ Common exam trap
Microsoft often tests the misconception that GPU or specialized compute is required for AI workloads, but in this scenario, the heavy lifting is done by Azure OpenAI's API, so the focus should be on cost-effective, scalable compute for orchestration, not local GPU 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 Functions with a consumption plan to trigger processing jobs and call Azure OpenAI.
Azure Functions with a consumption plan provides a serverless, event-driven architecture that scales automatically to handle variable workloads, ensuring cost-effectiveness by charging only for compute time used. This architecture is ideal for processing large volumes of PDFs, as each document can trigger a function execution that calls Azure OpenAI for summarization, without the need for always-on 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.
- ✗
Use Azure Kubernetes Service (AKS) with a persistent node pool of GPU nodes.
Why it's wrong here
A persistent GPU node pool bills for reserved nodes around the clock regardless of demand, defeating cost-effectiveness for variable workloads. It is tempting for hosting custom models needing sustained GPU capacity, but summarisation here uses Azure OpenAI, so no GPU nodes are needed.
- ✓
Use Azure Functions with a consumption plan to trigger processing jobs and call Azure OpenAI.
Why this is correct
Azure Functions on a consumption plan scales automatically and bills per execution, matching the stem's cost-effectiveness and variable-workload constraints. It triggers PDF processing jobs that call Azure OpenAI, avoiding idle capacity charges that always-on compute would incur.
- ✗
Deploy a GPU-enabled virtual machine and run the summarization jobs sequentially.
Why it's wrong here
A GPU virtual machine runs continuously, billing even when idle, and sequential job execution cannot absorb variable workloads. It is tempting when you need full control over model hosting, but Azure OpenAI is consumed as a managed service, so provisioning GPUs adds cost without benefit.
- ✗
Use Azure Logic Apps to iterate through documents and call Azure OpenAI.
Why it's wrong here
Logic Apps executes workflow actions per document with fixed connector and run costs, and its sequential orchestration does not scale economically for large variable volumes. It is tempting for lightweight document-triggered automation, but it lacks the queue-driven elastic scaling the workload requires.
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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This AI-102 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 AI-102 exam.