- A
Adjust the prompt to be more specific and include few-shot examples.
Clear prompts with examples guide the model to produce accurate, relevant summaries.
- B
Increase the temperature parameter.
Why wrong: Increasing temperature increases randomness, which can worsen hallucinations.
- C
Use a different base model.
Why wrong: Changing the base model might help but is not the first step; prompt engineering should be tried first.
- D
Increase the max tokens.
Why wrong: Max tokens controls output length, not accuracy or hallucinations.
Quick Answer
The correct first step is to adjust the prompt to be more specific and include few-shot examples. This approach directly addresses hallucinations by providing the model with clear, constrained instructions and concrete examples of desired outputs, which anchor the generation to factual patterns rather than allowing it to invent details. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this concept tests your understanding that prompt engineering is the least invasive and most effective lever for improving accuracy before changing model parameters or architecture. A common trap is confusing temperature reduction with specificity—while lowering temperature reduces randomness, it does not correct factual gaps like a well-structured prompt with few-shot examples does. Remember the memory tip: “Specific prompts and examples are the anchor; temperature and tokens are just the rudder.”
1Z0-1127 Using OCI Generative AI Service Practice Question
This 1Z0-1127 practice question tests your understanding of using oci generative ai service. 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 uses OCI Generative AI service for customer support summarization. They notice the model frequently misses key details and generates hallucinations. What should they do first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
Adjust the prompt to be more specific and include few-shot examples.
Option C is correct because improving prompt engineering with specific instructions and few-shot examples reduces hallucinations and improves accuracy. Option A is wrong because increasing temperature increases randomness, making hallucinations worse. Option B is wrong because switching models is a more drastic step that may not address the root cause. Option D is wrong because increasing max tokens does not improve accuracy.
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.
- ✓
Adjust the prompt to be more specific and include few-shot examples.
Why this is correct
Clear prompts with examples guide the model to produce accurate, relevant summaries.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
Increase the temperature parameter.
Why it's wrong here
Increasing temperature increases randomness, which can worsen hallucinations.
- ✗
Use a different base model.
Why it's wrong here
Changing the base model might help but is not the first step; prompt engineering should be tried first.
- ✗
Increase the max tokens.
Why it's wrong here
Max tokens controls output length, not accuracy or 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
Max tokens controls output length, not accuracy or hallucinations.
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 small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
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 1Z0-1127 NAT questions on configuration and troubleshooting.
- →
Using OCI Generative AI Service — study guide chapter
Learn the concepts, then practise the questions
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Using OCI Generative AI Service practice questions
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
Using OCI Generative AI Service — This question tests Using OCI Generative AI Service — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Adjust the prompt to be more specific and include few-shot examples. — Option C is correct because improving prompt engineering with specific instructions and few-shot examples reduces hallucinations and improves accuracy. Option A is wrong because increasing temperature increases randomness, making hallucinations worse. Option B is wrong because switching models is a more drastic step that may not address the root cause. Option D is wrong because increasing max tokens does not improve accuracy.
What should I do if I get this 1Z0-1127 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 1Z0-1127 NAT questions on configuration and troubleshooting.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 23, 2026
This 1Z0-1127 practice question is part of Courseiva's free Oracle certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the 1Z0-1127 exam.
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