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 a large language model to generate answers to employee questions about internal HR policies. However, the model sometimes produces answers that are factually incorrect or not based on the official policies. To reduce these inaccuracies, the company wants to provide the model with relevant, up-to-date policy documents as extra context before generating a response. Which technique is being applied?
Why wrong: Prompt engineering carefully phrases the input, but without providing specific external documents as context, it is less effective at reducing hallucinations.
B
Fine-tuning the model on policy documents
Why wrong: Fine-tuning retrains the model on new data, which is a different approach than providing documents at inference time as context.
C
Grounding with relevant data (RAG)
Grounding, or RAG, retrieves relevant external documents and includes them in the prompt context, which helps the model generate factually accurate answers.
D
Using a content filter
Why wrong: Content filters block harmful, offensive, or sensitive content but do not improve the factual accuracy of responses.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Grounding with relevant data (RAG)
The technique described is Retrieval-Augmented Generation (RAG), which retrieves relevant, up-to-date policy documents from an external knowledge base and provides them as context to the large language model before generating a response. This grounds the model's output in verified data, reducing factual inaccuracies without modifying the model itself. Option C is correct because RAG directly addresses the need to supply extra context from authoritative sources.
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 only
Why it's wrong here
Prompt engineering carefully phrases the input, but without providing specific external documents as context, it is less effective at reducing hallucinations.
✗
Fine-tuning the model on policy documents
Why it's wrong here
Fine-tuning retrains the model on new data, which is a different approach than providing documents at inference time as context.
✓
Grounding with relevant data (RAG)
Why this is correct
Grounding, or RAG, retrieves relevant external documents and includes them in the prompt context, which helps the model generate factually accurate answers.
Related concept
Read the scenario before looking for a memorised answer.
✗
Using a content filter
Why it's wrong here
Content filters block harmful, offensive, or sensitive content but do not improve the factual accuracy of responses.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse fine-tuning (which modifies the model) with RAG (which augments the prompt with external data), or assume prompt engineering alone can inject new information, when in fact RAG is the specific technique for grounding with external, up-to-date documents.
Trap categories for this question
Keyword trap
Prompt engineering carefully phrases the input, but without providing specific external documents as context, it is less effective at reducing hallucinations.
Detailed technical explanation
How to think about this question
Under the hood, RAG uses a vector database (e.g., Azure Cognitive Search) to index policy documents into embeddings, then performs a similarity search against the user's query to retrieve the most relevant chunks. These chunks are prepended to the prompt as context, allowing the LLM to generate answers grounded in the retrieved data. A subtle behavior is that RAG can still produce hallucinations if the retrieved context is irrelevant or if the model overrides the context with its parametric knowledge, so careful chunking and prompt design are critical.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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 with relevant data (RAG) — The technique described is Retrieval-Augmented Generation (RAG), which retrieves relevant, up-to-date policy documents from an external knowledge base and provides them as context to the large language model before generating a response. This grounds the model's output in verified data, reducing factual inaccuracies without modifying the model itself. Option C is correct because RAG directly addresses the need to supply extra context from authoritative sources.
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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Question Discussion
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