- A
Prompt Engineering
Why wrong: Prompt engineering improves output by carefully designing the input text, but it does not automatically inject external, up-to-date knowledge unless the prompt explicitly includes it, which is less systematic than grounding.
- B
Grounding
Grounding connects the model to external data sources (like product documentation) to provide factual context, significantly reducing hallucinations and improving accuracy.
- C
Fine-tuning
Why wrong: Fine-tuning adjusts the model's weights on a specific dataset, which can improve domain knowledge but is computationally expensive and may not incorporate rapidly changing information without retraining.
- D
Reinforcement Learning from Human Feedback (RLHF)
Why wrong: RLHF uses human ratings to align the model with preferences (e.g., helpfulness), but it does not directly supply factual context; grounding is the method for injecting real-time, reliable information.
Quick Answer
The correct answer is grounding. This technique directly addresses the problem of hallucinations by anchoring the generative AI model’s output to a specific, authoritative source of external data—in this case, the company’s up-to-date product documentation. By providing this context before generation, the model is constrained to base its responses on verified facts rather than relying solely on its internal training data, which may be outdated or incomplete. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how to improve response accuracy in enterprise scenarios, often appearing as a contrast to fine-tuning or prompt engineering. A common trap is confusing grounding with retrieval-augmented generation (RAG), but remember: grounding is the broader principle of supplying external data as context, while RAG is a specific implementation. Memory tip: think of grounding as “tying the model down to the facts” to prevent it from drifting into fabrication.
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 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?
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.
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.
- ✗
Prompt Engineering
Why it's wrong here
Prompt engineering improves output by carefully designing the input text, but it does not automatically inject external, up-to-date knowledge unless the prompt explicitly includes it, which is less systematic than grounding.
- ✓
Grounding
Why this is correct
Grounding connects the model to external data sources (like product documentation) to provide factual context, significantly reducing hallucinations and improving accuracy.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Fine-tuning
Why it's wrong here
Fine-tuning adjusts the model's weights on a specific dataset, which can improve domain knowledge but is computationally expensive and may not incorporate rapidly changing information without retraining.
- ✗
Reinforcement Learning from Human Feedback (RLHF)
Why it's wrong here
RLHF uses human ratings to align the model with preferences (e.g., helpfulness), but it does not directly supply factual context; grounding is the method for injecting real-time, reliable information.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Trap categories for this question
Command / output trap
Prompt engineering improves output by carefully designing the input text, but it does not automatically inject external, up-to-date knowledge unless the prompt explicitly includes it, which is less systematic than grounding.
Detailed technical explanation
How to think about this question
Grounding is often implemented via retrieval-augmented generation (RAG), where a vector search retrieves relevant document chunks from a knowledge base (e.g., Azure AI Search) and injects them into the model's prompt context window. This technique leverages the model's in-context learning ability without modifying its weights, making it ideal for dynamic, up-to-date information. A real-world scenario is a customer support chatbot that queries a product documentation index for each user question, ensuring responses reflect the latest specifications.
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: 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.
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.
About these practice questions
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Same concept, more angles
1 more ways this is tested on AI-900
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. A marketing agency wants to use Azure OpenAI Service to generate product descriptions. They need the descriptions to be factually accurate and based on their specific product catalog, which is stored in a vector database. Which technique should they use to ground the model's outputs in their own data?
medium- A.Fine-tuning the model on the product catalog
- ✓ B.Prompt engineering with retrieval augmented generation (RAG)
- C.Zero-shot prompting without additional data
- D.Reinforcement learning from human feedback (RLHF)
Why B: Retrieval augmented generation (RAG) is the correct technique because it allows the model to retrieve relevant, up-to-date product information from the vector database at inference time and use that data as context to generate factually accurate descriptions. This grounds the model's outputs in the specific product catalog without modifying the underlying model weights, ensuring responses are based on the agency's own data.
Last reviewed: Jun 30, 2026
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