AI-900 Practice Question: Describe features of generative AI workloads on Azure
A company uses a generative AI model to answer customer questions about their products. They observe that the model sometimes produces factually incorrect or fabricated information. To reduce these inaccuracies, they want to provide the model with relevant, up-to-date product documentation as context before generating a response. Which technique is being applied?
⚠ Common exam trap
Microsoft often tests the distinction between grounding (providing external context at inference time) and fine-tuning (updating model weights), so candidates mistakenly choose fine-tuning when the scenario describes adding new information without 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
✓
Grounding
B is correct because grounding is the technique of providing a generative AI model with specific, authoritative source data (such as product documentation) as context before generating a response. This anchors the model's output to verified facts, directly reducing hallucinations and fabricated information by constraining the generation to the provided context.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Prompt Engineering
Why it's wrong here
Prompt engineering crafts the input query or context window to steer the model's output, but it only leverages knowledge already baked into the model's static parameters. To inject fresh or proprietary product documentation, a prompt would need to manually paste the text each time, which is unsystematic, token-limited, and not a true architectural fix. It can reduce stylistic errors but does not automatically connect to an external data source, so it is far less robust than grounding for factual accuracy.
- ✓
Grounding
Why this is correct
Grounding anchors the generative model's decoding process to a curated external knowledge source, such as product documentation or a vectorized enterprise search index. During inference, the system retrieves relevant passages and inserts them into the prompt context, forcing the model to base its answer on those verifiable facts rather than on unconstrained parametric memory. This significantly lowers hallucination risk and improves accuracy and traceability, which is exactly why it is the correct method for a customer-answer system needing up-to-date, domain-specific information.
- ✗
Fine-tuning
Why it's wrong here
Fine-tuning updates the model's weights via supervised learning on a labeled dataset of domain-specific question-answer pairs, thereby internalizing patterns from that dataset. However, it requires a large, high-quality corpus, substantial compute, and a full retraining cycle whenever facts change, making it poor for rapidly evolving product knowledge. It can also introduce catastrophic forgetting or overfitting, and it does not provide a direct citation or retrieval link, so it is less suitable than grounding for real-time factual accuracy.
- ✗
Reinforcement Learning from Human Feedback (RLHF)
Why it's wrong here
Reinforcement Learning from Human Feedback (RLHF) trains a reward model from human comparisons of model outputs to optimize for subjective qualities like helpfulness, safety, and alignment with user preferences. It adjusts the policy's behavior but does not load any new factual content into the model, so it cannot fix hallucinations about current product details. RLHF is complementary to grounding—it shapes tone and style while grounding supplies the evidence—but on its own it lacks the external knowledge injection this scenario demands.
Go deeper
Related to this question
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What is Generative AI?
Key term
Azure OpenAI Service
Azure OpenAI Service is a cloud platform from Microsoft that lets developers use powerful artificial intelligence models, like GPT-4, to build applications that can understand and generate human-like text, code, images, and more.
Key term
Generative AI
Generative AI is a type of artificial intelligence that creates new content—like text, images, or code—by learning patterns from existing data.
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