- A
Option C
Why wrong: Fine-tuning is unnecessary and expensive.
- B
Option D
Why wrong: A single prompt for all products is not feasible due to token limits.
- C
Option A
Why wrong: Few-shot learning for each product is token-inefficient and costly.
- D
Option B
Prompt templates with attributes are cost-effective and scalable.
Question 735 of 993
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 a machine learning engineer at a large retail company. The company has thousands of product descriptions that need to be updated regularly. They currently use a manual process. You propose using Azure OpenAI to generate new descriptions based on product attributes. You have a dataset of existing product descriptions and attributes stored in an Azure SQL Database. The solution must be cost-effective, scalable, and must not require retraining the model. You need to design the solution. You have the following options:
Option A: Use Azure OpenAI with few-shot learning by including examples in the prompt for each product. Deploy the model on an Azure Kubernetes Service (AKS) cluster for high throughput.
Option B: Use Azure OpenAI with prompt templates that include product attributes and call the API for each product. Use Azure Logic Apps to orchestrate the workflow and store results back to Azure SQL Database.
Option C: Fine-tune a custom model on the existing product descriptions and deploy it as a managed endpoint. Use Azure Data Factory to batch process all products.
Option D: Use Azure OpenAI with the batch API to generate descriptions for all products at once, using a single prompt that lists all products and attributes. Store the batch output in Azure Blob Storage and then import into Azure SQL Database.
Which option should you choose?
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
✓
Option B
Option B (Azure Logic Apps) is the correct choice. It uses Azure OpenAI with prompt templates that insert product attributes, making individual API calls per product. This approach is scalable because Azure Logic Apps can handle high volumes with built-in retry and concurrency, and it is cost-effective as you only pay per API call and execution. It does not require model retraining. In contrast, Option A (AKS) introduces unnecessary infrastructure complexity; Option C (fine-tuning) requires retraining; and Option D (batch API) risks exceeding prompt size limits and is less suitable for incremental updates.
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.
- ✗
Option C
Why it's wrong here
Fine-tuning is unnecessary and expensive.
- ✗
Option D
Why it's wrong here
A single prompt for all products is not feasible due to token limits.
- ✗
Option A
Why it's wrong here
Few-shot learning for each product is token-inefficient and costly.
- ✓
Option B
Why this is correct
Prompt templates with attributes are cost-effective and scalable.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap is that candidates may mistakenly choose Option A (AKS) thinking it provides better scalability, Option C (fine-tuning) for customization, or Option D (batch API) for efficiency, but they overlook that Option B (Azure Logic Apps) offers the right balance of cost-effectiveness, scalability, and no retraining for incremental updates.
Detailed technical explanation
How to think about this question
Azure OpenAI's batch API allows submitting multiple prompts in a single request, which is processed asynchronously and billed at a lower rate per token compared to real-time API calls. The batch output is stored in a structured format (e.g., JSONL) in Azure Blob Storage, enabling efficient import into Azure SQL Database via PolyBase or COPY INTO. This approach avoids per-call latency and throttling limits, making it ideal for high-volume, non-real-time workloads like updating thousands of product descriptions.
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.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
What to study next
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FAQ
Questions learners often ask
What does this AI-102 question test?
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: Option B — Option B (Azure Logic Apps) is the correct choice. It uses Azure OpenAI with prompt templates that insert product attributes, making individual API calls per product. This approach is scalable because Azure Logic Apps can handle high volumes with built-in retry and concurrency, and it is cost-effective as you only pay per API call and execution. It does not require model retraining. In contrast, Option A (AKS) introduces unnecessary infrastructure complexity; Option C (fine-tuning) requires retraining; and Option D (batch API) risks exceeding prompt size limits and is less suitable for incremental updates.
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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Last reviewed: Jun 11, 2026
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