The answer is Azure Functions with a consumption plan, as this serverless architecture provides the most cost-effective solution for processing PDFs with Azure OpenAI. The consumption plan’s event-driven model automatically scales to handle variable workloads, charging only for the compute time consumed per function execution, which eliminates the cost of idle, always-on infrastructure. For the AI-102 exam, this scenario tests your understanding of how to pair serverless compute with Azure OpenAI to optimize cost under unpredictable demand, often appearing as a distractor against always-on App Service or batch-based solutions. A common trap is choosing a dedicated plan for “control,” but the consumption plan’s pay-per-execution model is explicitly designed for bursty, document-triggered jobs. Remember the mnemonic “FOCuS” — Functions, On-demand, Consumption, Serverless — to recall that serverless is the most cost-effective choice for variable workloads.
AI-102 Implement generative AI solutions Practice Question
This AI-102 practice question tests your understanding of implement generative ai solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
AKS adds operational overhead and cost; not optimal for variable batch processing.
✓
Use Azure Functions with a consumption plan to trigger processing jobs and call Azure OpenAI.
Why this is correct
Serverless functions scale automatically and you pay only for compute time.
Related concept
Read the scenario before looking for a memorised answer.
✗
Deploy a GPU-enabled virtual machine and run the summarization jobs sequentially.
Why it's wrong here
A VM incurs fixed costs and does not scale efficiently for variable workloads.
✗
Use Azure Logic Apps to iterate through documents and call Azure OpenAI.
Why it's wrong here
Logic Apps are designed for integration workflows, not heavy compute tasks.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Azure Functions on a consumption plan uses a dynamic scaling model where the number of function instances scales out based on the number of incoming events (e.g., blob triggers for PDF uploads), and scales to zero when idle, ensuring no cost for idle capacity. Each function instance can call the Azure OpenAI service via REST API, and the consumption plan's execution time limit (default 5 minutes, configurable up to 10 minutes) is sufficient for summarization tasks, while the 1 GB memory allocation per instance handles typical PDF processing libraries like iText or PyMuPDF.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Azure Functions with a consumption plan to trigger processing jobs and call Azure OpenAI. — Option B is correct because 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.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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