- A
Fine-tuning the model on a curated dataset
Why wrong: Fine-tuning would retrain the model, which contradicts the requirement of not retraining the model. It also requires additional labeled data and compute resources.
- B
Prompt engineering with specific instructions to avoid stereotypes
By including explicit instructions in the prompt (e.g., 'Do not include any stereotypes'), the model can be guided to produce safer outputs without modifying its underlying weights.
- C
Reducing the temperature parameter to zero
Why wrong: Temperature controls the randomness of token selection; setting it to zero makes outputs deterministic but does not address the knowledge or biases embedded in the model, so stereotypes may still appear.
- D
Increasing the maximum output length
Why wrong: Increasing max output length allows the model to generate longer text, which could actually increase the chance of including harmful stereotypes rather than preventing them.
Quick Answer
The correct approach is prompt engineering with specific instructions to avoid stereotypes, because it allows you to guide the model’s behavior at inference time without modifying its underlying weights. By crafting the input to include explicit directives—such as “Avoid harmful stereotypes” or “Generate inclusive content”—you steer the output toward safer, more appropriate text, directly addressing the undesired outputs through input design alone. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of how to mitigate model risks without retraining, a key concept in responsible AI and prompt engineering. A common trap is assuming you must fine-tune or filter outputs post-generation, but the exam emphasizes that prompt engineering is the most efficient, cost-effective solution when retraining is off the table. Memory tip: think “Prompt first, not retrain”—the instruction lives in the prompt, not the model.
AI-900 Practice Question: Describe features of generative AI workloads on Azure
This AI-900 practice question tests your understanding of describe features of generative ai workloads on azure. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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.
A company uses a GPT-based model to generate marketing copy. They notice the model occasionally produces text that includes harmful stereotypes. They want to reduce these harmful outputs without retraining the model. Which approach is most appropriate?
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
Prompt engineering with specific instructions to avoid stereotypes
Option B is correct because prompt engineering allows you to guide the model's behavior at inference time without modifying its weights. By including explicit instructions in the prompt (e.g., 'Avoid harmful stereotypes'), you can steer the output toward safer content. This is the most appropriate approach when retraining is not an option, as it directly addresses the undesired outputs through input design.
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.
- ✗
Fine-tuning the model on a curated dataset
Why it's wrong here
Fine-tuning would retrain the model, which contradicts the requirement of not retraining the model. It also requires additional labeled data and compute resources.
- ✓
Prompt engineering with specific instructions to avoid stereotypes
Why this is correct
By including explicit instructions in the prompt (e.g., 'Do not include any stereotypes'), the model can be guided to produce safer outputs without modifying its underlying weights.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reducing the temperature parameter to zero
Why it's wrong here
Temperature controls the randomness of token selection; setting it to zero makes outputs deterministic but does not address the knowledge or biases embedded in the model, so stereotypes may still appear.
- ✗
Increasing the maximum output length
Why it's wrong here
Increasing max output length allows the model to generate longer text, which could actually increase the chance of including harmful stereotypes rather than preventing them.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse fine-tuning (which requires retraining) with prompt engineering (which does not), or assume that adjusting parameters like temperature or max tokens can fix content quality issues, when in fact they only affect randomness and length, not semantic safety.
Trap categories for this question
Command / output trap
Temperature controls the randomness of token selection; setting it to zero makes outputs deterministic but does not address the knowledge or biases embedded in the model, so stereotypes may still appear.
Detailed technical explanation
How to think about this question
Prompt engineering leverages the model's in-context learning ability, where the prompt acts as a conditional instruction that biases the generation distribution. For GPT-based models, system messages or user prompts with explicit constraints (e.g., 'Do not include stereotypes') can significantly reduce harmful outputs by adjusting the probability of token sequences. In Azure OpenAI Service, this is often combined with content filtering and safety systems, but prompt engineering remains a lightweight, no-retraining method to shape outputs.
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
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Prompt engineering with specific instructions to avoid stereotypes — Option B is correct because prompt engineering allows you to guide the model's behavior at inference time without modifying its weights. By including explicit instructions in the prompt (e.g., 'Avoid harmful stereotypes'), you can steer the output toward safer content. This is the most appropriate approach when retraining is not an option, as it directly addresses the undesired outputs through input design.
What should I do if I get this AI-900 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
This AI-900 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-900 exam.
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