The correct technique is few-shot prompting, because it provides the Azure OpenAI model with explicit examples of verbose emails paired with their desired concise summaries, teaching it the exact format, tone, and level of detail required for consistent output. Without these examples, the model defaults to its training distribution and often produces overly verbose or irrelevant summaries, which defeats the purpose of a production summarization pipeline. On the Microsoft Azure AI Engineer Associate AI-102 exam, this question tests your understanding of how to control output quality through prompt engineering, specifically distinguishing few-shot from zero-shot or chain-of-thought approaches. A common trap is choosing zero-shot prompting, which lacks the guiding examples needed for strict conciseness. Remember the memory tip: “Show, don’t just tell”—few-shot shows the model what you want, while zero-shot only tells it.
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 using Azure OpenAI Service to summarize customer emails. The summaries must be concise and contain only key information. Which prompt engineering technique should you apply?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use few-shot prompting with examples of desired summaries
Few-shot prompting is the correct technique because it provides the model with explicit examples of desired input-output pairs (e.g., a verbose email and its concise summary). This guides the model to learn the exact format, tone, and level of detail required for the summaries, which is critical for consistency in a production summarization pipeline. Without examples, the model may default to its training distribution and produce overly verbose or irrelevant output.
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 few-shot prompting with examples of desired summaries
Why this is correct
Few-shot examples guide the model to produce concise summaries.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use chain-of-thought prompting
Why it's wrong here
Chain-of-thought is for reasoning, not for summarization.
✗
Use zero-shot prompting with a one-sentence instruction
Why it's wrong here
Zero-shot may produce inconsistent summaries.
✗
Use negative prompting to avoid verbose output
Why it's wrong here
Negative prompting is less reliable than positive examples.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume a simple instruction (zero-shot) is sufficient for summarization, underestimating how much the model relies on explicit examples to enforce output structure and conciseness, especially when the task requires domain-specific key information extraction.
Detailed technical explanation
How to think about this question
Under the hood, few-shot prompting works by conditioning the model's next-token predictions on the provided examples, effectively shifting the probability distribution toward the desired output format. In Azure OpenAI, the number of examples (typically 3–5) must be balanced against the token limit; too many examples may consume context window space needed for the actual email. A real-world scenario is a customer support system where emails vary in length and topic; few-shot examples help the model consistently extract entities like order IDs and issue types while ignoring boilerplate.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 few-shot prompting with examples of desired summaries — Few-shot prompting is the correct technique because it provides the model with explicit examples of desired input-output pairs (e.g., a verbose email and its concise summary). This guides the model to learn the exact format, tone, and level of detail required for the summaries, which is critical for consistency in a production summarization pipeline. Without examples, the model may default to its training distribution and produce overly verbose or irrelevant output.
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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Question Discussion
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