Question 873 of 988

Implement generative AI solutions →mediumMultiple ChoiceObjective-mapped

AI-102 Implement generative AI solutions Practice Question

This AI-102 practice question tests your understanding of implement generative ai solutions. 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.

You are building a customer support chatbot using Azure OpenAI Service. The chatbot must only respond based on the company's product documentation and should not generate answers outside that scope. Which approach should you use?

Question 1mediummultiple choice

Full question →

A
Implement content filters to block responses not found in the documentation.
Why wrong: Content filters are designed to block offensive or harmful content, not to enforce domain-specific grounding.
B
Fine-tune a GPT-4 model on the product documentation.
Why wrong: Fine-tuning adapts the model but does not guarantee responses are limited to the documentation; the model can still hallucinate.
C
Use Azure OpenAI On Your Data with a search index built from the documentation.
This approach grounds the model on the indexed documents, ensuring responses are based on the documentation.
D
Use prompt engineering with a system message instructing the model to only answer from the documentation.
Why wrong: Prompt engineering is insufficient to prevent hallucination; the model may still generate information outside the provided documents.

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

✓

Use Azure OpenAI On Your Data with a search index built from the documentation.

Option C is correct because Azure OpenAI On Your Data with a search index ensures the model only generates responses grounded in the provided documents. Option A is wrong because fine-tuning alone does not prevent the model from generating ungrounded content. Option B is wrong because prompt engineering may still result in hallucination. Option D is wrong because content filters block harmful content but do not enforce domain grounding.

Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Implement content filters to block responses not found in the documentation.
Why it's wrong here
Content filters are designed to block offensive or harmful content, not to enforce domain-specific grounding.
✗
Fine-tune a GPT-4 model on the product documentation.
Why it's wrong here
Fine-tuning adapts the model but does not guarantee responses are limited to the documentation; the model can still hallucinate.
✓
Use Azure OpenAI On Your Data with a search index built from the documentation.
Why this is correct
This approach grounds the model on the indexed documents, ensuring responses are based on the documentation.
Related concept
Static NAT maps one inside address to one outside address.
✗
Use prompt engineering with a system message instructing the model to only answer from the documentation.
Why it's wrong here
Prompt engineering is insufficient to prevent hallucination; the model may still generate information outside the provided documents.

Common exam traps

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.

Detailed technical explanation

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.

Key takeaway

NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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.

Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.

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FAQ

Questions learners often ask

What does this AI-102 question test?

Implement generative AI solutions — This question tests Implement generative AI solutions — Static NAT maps one inside address to one outside address..

What is the correct answer to this question?

The correct answer is: Use Azure OpenAI On Your Data with a search index built from the documentation. — Option C is correct because Azure OpenAI On Your Data with a search index ensures the model only generates responses grounded in the provided documents. Option A is wrong because fine-tuning alone does not prevent the model from generating ungrounded content. Option B is wrong because prompt engineering may still result in hallucination. Option D is wrong because content filters block harmful content but do not enforce domain grounding.

What should I do if I get this AI-102 question wrong?

What is the key concept behind this question?

Static NAT maps one inside address to one outside address.

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