Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
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?
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
Distractor review
Fine-tuning the model on the product catalog
Fine-tuning adjusts the model's weights using labeled data, but it does not dynamically retrieve current information from a database. The model may still hallucinate or use outdated information.
B
Best answer
Prompt engineering with retrieval augmented generation (RAG)
RAG retrieves relevant chunks from the vector database and adds them to the prompt, ensuring the model uses the latest, specific product details to generate accurate descriptions.
C
Distractor review
Zero-shot prompting without additional data
Zero-shot prompting relies solely on the model's internal knowledge, which may be generic or outdated, leading to inaccuracies for the specific product catalog.
D
Distractor review
Reinforcement learning from human feedback (RLHF)
RLHF aligns the model with human preferences through reward signals, but it does not provide a mechanism to retrieve and use live product data from a database.
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: Prompt engineering with retrieval augmented generation (RAG) — Retrieval Augmented Generation (RAG) retrieves relevant information from a user-provided data source (like a vector database) and injects it into the prompt to the language model. This grounds the model's response in the retrieved facts, improving accuracy and reducing hallucination. Fine-tuning updates the model weights but doesn't automatically incorporate a live database. Zero-shot learning does not use examples. Reinforcement learning is for skill improvement, not factual grounding.
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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