Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
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?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Best answer
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
Distractor review
Increase the temperature parameter to 1.0 to force more creative responses
Increasing temperature makes the model more creative and random, which would increase the likelihood of generating incorrect or irrelevant information, not reduce it.
C
Distractor review
Fine-tune the model on the knowledge base documents using supervised learning
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
Distractor review
Use prompt engineering with a system message that tells the model to never make up facts
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 trap
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Technical deep dive
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
Question 1
A developer wants to build a virtual assistant that can understand user intents such as 'Book a flight' or 'Check weather' and extract relevant entities like destination and date. The developer has a small set of labeled example utterances. Which Azure AI Language feature should the developer use?
Question 2
A developer is building a customer support chatbot using Azure OpenAI. The chatbot should never reveal its system instructions or internal configuration. The developer wants to add a rule at the beginning of the conversation to prevent prompt injection attacks. Which technique should they use?
Question 3
A developer is using Azure OpenAI Service to generate product descriptions from technical specifications. The generated descriptions sometimes include plausible-sounding but incorrect details (hallucinations). The developer wants to ensure the model's responses are strictly based on the provided product data and does not add any external or invented information. Which approach should the developer use?
Question 4
A developer is using Azure OpenAI with GPT-4 to build a chatbot that answers legal questions based on a company's internal policy documents. The developer wants the model's responses to be maximally deterministic and factual, avoiding any creative or speculative language. Which parameter should the developer set to the lowest possible value in the API call?
Question 5
A developer is using Azure OpenAI to generate creative product descriptions. The outputs are often repetitive and lack variety. The developer wants to increase the diversity of the generated text while still keeping it coherent. Which parameter should the developer increase?
Question 6
A developer is using Azure OpenAI Service to generate product descriptions. They want the output to be highly focused and deterministic, with less randomness. Which parameter should they decrease?
FAQ
Questions learners often ask
What does this AI-900 question test?
Static NAT maps one inside address to one outside address.
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 — Language models can sometimes generate text that is factually incorrect but sounds convincing, known as hallucination. Retrieval Augmented Generation (RAG) is a technique where the model is given relevant document excerpts as context before generating an answer. This grounds the response in the provided information, greatly reducing hallucinations. Increasing temperature would make output more random, which is counterproductive. Fine-tuning on the documents does help but is not as focused on retrieval and may still lead to hallucinations for unseen queries. Prompt engineering alone without knowledge retrieval is insufficient to guarantee factual accuracy.
What should I do if I get this AI-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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