AI-900 Practice Question: Describe features of generative AI workloads on Azure
A company uses Azure OpenAI Service to generate marketing copy for a new product. They have a strict brand voice that requires formal, technical language and explicitly prohibits any humorous or informal phrases. They want to enforce these constraints without retraining the model. Which technique should they use?
⚠ Common exam trap
A common mix-up: candidates confuse fine-tuning (which requires retraining) with prompt engineering (which is inference-only), leading them to select fine-tuning when the question explicitly prohibits retraining.
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
✓
B) Prompt engineering
Prompt engineering is correct because it allows the user to craft system messages or user prompts that explicitly instruct the model to use formal, technical language and avoid humor, all without modifying the underlying model weights. This technique leverages the model's instruction-following capability to enforce constraints at inference time, making it ideal for brand voice enforcement without retraining.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A) Fine-tuning
Why it's wrong here
Fine-tuning updates the model's weights by training on a curated, domain-specific dataset using gradient descent. This process requires substantial labeled data and compute resources, and it permanently alters the model's behavior. In this scenario, the company wants to avoid retraining, so fine-tuning is an unnecessary and costly approach to enforce brand voice.
- ✓
B) Prompt engineering
Why this is correct
Prompt engineering controls model output by designing the input prompt, including system messages, instructions, and few-shot examples, to specify tone, style, and brand constraints. It requires no weight updates or labeled datasets; instead, it leverages the model's existing language understanding at inference time. This is the lightest-weight, fastest, and most cost-effective way to guide Azure OpenAI's text generation for marketing copy.
- ✗
C) Reinforcement learning
Why it's wrong here
Reinforcement learning (RL) trains an agent by rewarding or penalizing actions to maximize cumulative reward, and in LLMs it appears as RLHF (reinforcement learning from human feedback) during post-training. It involves building a reward model, running iterative training cycles, and updating the model's parameters—entirely separate from a single inference request. The scenario explicitly forbids retraining, and RL cannot be applied through a static prompt; it is a model-alteration technique.
- ✗
D) Transfer learning
Why it's wrong here
Transfer learning is the broad paradigm of applying a pre-trained model to a new task, often by fine-tuning its weights on task-specific data. While prompt engineering technically transfers knowledge from a pre-trained model without altering weights, the term typically implies additional training or adaptation. Choosing transfer learning would incorrectly suggest modifying the model rather than simply crafting a prompt, so it does not fit the company's no-retraining constraint.
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Azure Machine Learning Studio
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.