Question 711 of 1,020

Describe features of generative AI workloads on Azure →mediumMultiple ChoiceObjective-mapped

Quick Answer

The correct answer is to implement Retrieval Augmented Generation (RAG) with Azure OpenAI. This approach directly minimizes hallucinations by grounding the model’s responses in your own knowledge base documents, rather than relying solely on the model’s internal training data. When a user asks a question, RAG retrieves the most relevant document excerpts and injects them into the prompt as context, forcing the assistant to answer exclusively from those provided facts. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of how to control generative AI outputs and avoid fabrications—a key concept in responsible AI. A common trap is confusing RAG with fine-tuning; remember that fine-tuning adjusts the model’s weights, while RAG dynamically supplies external context without altering the model itself. For a memory tip, think “RAG = Retrieve And Ground” to recall that it retrieves documents and grounds the answer in them, preventing the chatbot from making up plausible-sounding but incorrect information.

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. 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.

A company uses Azure OpenAI Service to power a chat-based support assistant. They have extensive knowledge base documents that contain the correct information. The company wants the assistant to answer questions solely based on the provided documents and avoid generating plausible-sounding but incorrect information. Which approach should they implement to minimize the risk of such fabrications?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

Question 1mediummultiple choice

Full question →

A
Retrieval Augmented Generation (RAG) — provide relevant document excerpts as context in the prompt
RAG supplies the model with pertinent knowledge from the documents at query time, ensuring the answer is grounded in the provided content and significantly reducing hallucinations.
B
Increase the temperature parameter to 1.0 to force more creative responses
Why wrong: Increasing temperature makes the model more creative and random, which would increase the likelihood of generating incorrect or irrelevant information, not reduce it.
C
Fine-tune the model on the knowledge base documents using supervised learning
Why wrong: Fine-tuning teaches the model new facts but does not guarantee that it will only answer from those facts; it can still hallucinate, especially when asked questions outside the training data.
D
Use prompt engineering with a system message that tells the model to never make up facts
Why wrong: While helpful, a simple instruction is not reliable. Without actual document context, the model may still invent answers to fill gaps in knowledge.

Full breakdown with real-world context →

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

✓

Retrieval Augmented Generation (RAG) — provide relevant document excerpts as context in the prompt

Retrieval Augmented Generation (RAG) is the correct approach because it grounds the model's responses in actual, retrieved document excerpts provided as context in the prompt. This ensures the assistant answers based solely on the supplied knowledge base, directly minimizing the risk of hallucination (plausible-sounding but incorrect information) by constraining the model to the retrieved facts.

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.

✓
Retrieval Augmented Generation (RAG) — provide relevant document excerpts as context in the prompt
Why this is correct
RAG supplies the model with pertinent knowledge from the documents at query time, ensuring the answer is grounded in the provided content and significantly reducing hallucinations.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the temperature parameter to 1.0 to force more creative responses
Why it's wrong here
Increasing temperature makes the model more creative and random, which would increase the likelihood of generating incorrect or irrelevant information, not reduce it.
✗
Fine-tune the model on the knowledge base documents using supervised learning
Why it's wrong here
Fine-tuning teaches the model new facts but does not guarantee that it will only answer from those facts; it can still hallucinate, especially when asked questions outside the training data.
✗
Use prompt engineering with a system message that tells the model to never make up facts
Why it's wrong here
While helpful, a simple instruction is not reliable. Without actual document context, the model may still invent answers to fill gaps in knowledge.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often assume prompt engineering (Option D) or fine-tuning (Option C) are sufficient to prevent hallucinations, but without retrieval-based grounding, the model can still generate confident-sounding falsehoods from its internal knowledge.

Detailed technical explanation

How to think about this question

RAG works by embedding the knowledge base documents into a vector index (e.g., using Azure Cognitive Search), then at query time retrieving the top-k most relevant chunks via cosine similarity search. These chunks are injected into the prompt as context, effectively turning the LLM into a constrained reader that must answer from the provided text, reducing hallucination to near-zero for in-domain queries. In practice, this approach also handles out-of-scope questions gracefully by enabling the model to respond with 'I don't know' when no relevant document is retrieved.

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

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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: Retrieval Augmented Generation (RAG) — provide relevant document excerpts as context in the prompt — Retrieval Augmented Generation (RAG) is the correct approach because it grounds the model's responses in actual, retrieved document excerpts provided as context in the prompt. This ensures the assistant answers based solely on the supplied knowledge base, directly minimizing the risk of hallucination (plausible-sounding but incorrect information) by constraining the model to the retrieved facts.

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.

Are there clue words in this question I should notice?

Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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