Generative AI Leader Practice Question: Techniques to Improve Generative AI Model Output
This Generative AI Leader practice question tests your understanding of techniques to improve generative ai model output. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 is prompt engineering a model for customer support. They want to reduce hallucination (false information) in responses. Which TWO techniques are most effective? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement RAG to retrieve relevant documents for context
Correct options: B and D. B (Retrieval-Augmented Generation) grounds the model in real data. D (specify in system instruction to only use provided facts) instructs the model to rely on context. A (increase temperature) increases creativity, worsening hallucination. C (few-shot examples) helps format but not factuality. E (reduce max tokens) only limits length.
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 RAG to retrieve relevant documents for context
Static NAT maps one inside address to one outside address.
✗
Provide 3 few-shot examples of conversations
Why it's wrong here
Few-shot examples improve style but not factuality without grounding.
✗
Reduce max output tokens to 150
Why it's wrong here
Shorter output doesn't address the source of false information.
✓
Add a system instruction: 'Only answer based on the provided context.'
Why this is correct
This instructs the model to rely on given info, reducing hallucination.
Related concept
Static NAT maps one inside address to one outside address.
✗
Increase temperature to 1.2
Why it's wrong here
Higher temperature makes outputs more random, often increasing hallucinations.
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.
Trap categories for this question
Command / output trap
Shorter output doesn't address the source of false information.
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 Generative AI Leader NAT questions on configuration and troubleshooting.
What does this Generative AI Leader question test?
Techniques to Improve Generative AI Model Output — This question tests Techniques to Improve Generative AI Model Output — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Implement RAG to retrieve relevant documents for context — Correct options: B and D. B (Retrieval-Augmented Generation) grounds the model in real data. D (specify in system instruction to only use provided facts) instructs the model to rely on context. A (increase temperature) increases creativity, worsening hallucination. C (few-shot examples) helps format but not factuality. E (reduce max tokens) only limits length.
What should I do if I get this Generative AI Leader question wrong?
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 Generative AI Leader NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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