Quick Answer
The correct action is to include relevant product information in the system message. This works because providing ground truth data directly in the prompt grounds the model’s output in verified facts, a technique central to improving factual accuracy in Azure OpenAI output. By embedding specific product details—such as features, release dates, or specifications—into the system message, you effectively implement a form of retrieval-augmented generation (RAG) without external tools, forcing the model to rely on your authoritative source rather than its internal, potentially outdated knowledge. On the Microsoft Azure AI Engineer Associate AI-102 exam, this question tests your understanding of prompt engineering fundamentals, specifically how system messages set behavioral guardrails. A common trap is confusing temperature (which controls creativity) or maxTokens (which limits length) with accuracy—neither injects facts. Memory tip: think “System message = Source of truth” to remember that feeding the model correct data is the only direct path to factual precision.
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 generate marketing copy. You notice that the output sometimes contains factual inaccuracies about your company's products. Which action can you take to improve factual accuracy?
Lower the temperature to 0.
Why wrong: Temperature affects randomness, not factual accuracy.
Include relevant product information in the system message.
Providing accurate context in the prompt helps the model generate factual responses.
Increase the maxTokens to 4000.
Why wrong: maxTokens limits length, not accuracy.
Add a stop sequence to limit output.
Why wrong: Stop sequences control when generation stops, not accuracy.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Include relevant product information in the system message.
Option A is correct because providing ground truth data in the prompt (e.g., via RAG) improves accuracy. Option B is wrong as temperature affects creativity. Option C is wrong as maxTokens limits length. Option D is wrong as stop sequences control generation stopping.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Lower the temperature to 0.
Why it's wrong here
Temperature affects randomness, not factual accuracy.
Include relevant product information in the system message.
Why this is correct
Providing accurate context in the prompt helps the model generate factual responses.
Related concept
Read the scenario before looking for a memorised answer.
Increase the maxTokens to 4000.
Why it's wrong here
maxTokens limits length, not accuracy.
Add a stop sequence to limit output.
Why it's wrong here
Stop sequences control when generation stops, not accuracy.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Include relevant product information in the system message. — Option A is correct because providing ground truth data in the prompt (e.g., via RAG) improves accuracy. Option B is wrong as temperature affects creativity. Option C is wrong as maxTokens limits length. Option D is wrong as stop sequences control generation stopping.
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You are using Azure OpenAI Service to generate marketing copy. The marketing team reports that the generated content sometimes contains factual inaccuracies. You need to improve the factual accuracy of the generated content. What should you do?
mediumWhy B: Option B is correct because providing relevant context in the prompt gives the model factual information to base its response on. Option A is wrong because reducing temperature does not improve factual accuracy. Option C is wrong because increasing max_tokens does not improve accuracy. Option D is wrong because disabling content filtering does not address inaccuracies.
Last reviewed: Jun 20, 2026
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.
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