Question 277 of 500

Using OCI Generative AI Service →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is to reduce temperature to 0.2 and top_p to 0.5, then fine-tune the model on a curated dataset of 5,000 clinical summaries with a learning rate of 0.00005 and batch size of 8. This combination directly addresses clinical hallucination mitigation with parameter tuning and fine-tuning by lowering the model’s randomness—lower temperature and top_p force more deterministic, conservative token selection—while fine-tuning on domain-specific data anchors the model to factual clinical patterns, reducing invented diagnoses. On the Oracle Cloud Infrastructure Generative AI Professional 1Z0-1127 exam, this scenario tests your understanding that parameter tuning controls output creativity, whereas fine-tuning adapts the model’s knowledge base; a common trap is choosing a safety filter or increasing tokens, which do not fix factual accuracy. Remember the mnemonic “Low Temp, Low Top, Fine-Tune the Shop” to recall that reducing both parameters and retraining on curated data is the most effective path to reduce hallucinations without spiking latency.

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.

You are a cloud architect at a healthcare company that uses OCI Generative AI Service to analyze patient records and generate clinical summaries. The service is deployed in the Frankfurt region with a dedicated AI cluster. Recently, the compliance team flagged that some generated summaries contain hallucinated diagnoses not present in the source records. They demand immediate mitigation. The current setup uses the default model (cohere.command-r-08-2024) with temperature=0.7, top_p=0.9, and max_tokens=2048. The application sends the entire patient record as a single prompt. You have access to OCI Logging, monitoring metrics (latency, request count, token count, safety filter rejections), and the AI service's model fine-tuning capability. You must reduce hallucinations while minimizing latency increase. What is the most effective course of action?

Question 1hardmultiple choice

Read the full NAT/PAT explanation →

A
Switch to cohere.command-light model for faster inference and add a post-processing step using a BERT-based NER model to validate entities.
Why wrong: A lighter model may be faster but likely less accurate; post-processing NER helps but does not prevent hallucinations at generation time.
B
Increase max_tokens to 4096 and use chunked processing with overlapping context windows to provide more context.
Why wrong: Chunking with overlap may reduce hallucinations by providing more context, but increasing max_tokens increases latency and cost; the improvement might be marginal.
C
Enable the safety filter with strict content moderation and set up OCI Logging to audit all generations.
Why wrong: Safety filters block harmful content but do not reduce hallucinations about medical facts; auditing only detects issues after the fact.
D
Reduce temperature to 0.2, top_p to 0.5, and fine-tune the model on a curated dataset of 5,000 clinical summaries with a learning rate of 0.00005 and batch size of 8.
Lower temperature/top_p yields more deterministic outputs; fine-tuning on domain-specific data directly reduces hallucinations.

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

✓

Reduce temperature to 0.2, top_p to 0.5, and fine-tune the model on a curated dataset of 5,000 clinical summaries with a learning rate of 0.00005 and batch size of 8.

Option D is correct because reducing temperature and top_p makes the model more deterministic, reducing randomness and thus hallucinations. Fine-tuning on curated clinical data with a lower learning rate and smaller batch size aligns the model to the domain without excessive training. Option A might reduce hallucinations but increases latency and token cost. Option B only adds a safety filter, which does not address factual accuracy. Option C may change style but not reduce hallucinations.

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.

✗
Switch to cohere.command-light model for faster inference and add a post-processing step using a BERT-based NER model to validate entities.
Why it's wrong here
A lighter model may be faster but likely less accurate; post-processing NER helps but does not prevent hallucinations at generation time.
✗
Increase max_tokens to 4096 and use chunked processing with overlapping context windows to provide more context.
Why it's wrong here
Chunking with overlap may reduce hallucinations by providing more context, but increasing max_tokens increases latency and cost; the improvement might be marginal.
✗
Enable the safety filter with strict content moderation and set up OCI Logging to audit all generations.
Why it's wrong here
Safety filters block harmful content but do not reduce hallucinations about medical facts; auditing only detects issues after the fact.
✓
Reduce temperature to 0.2, top_p to 0.5, and fine-tune the model on a curated dataset of 5,000 clinical summaries with a learning rate of 0.00005 and batch size of 8.
Why this is correct
Lower temperature/top_p yields more deterministic outputs; fine-tuning on domain-specific data directly reduces hallucinations.
Related concept
Static NAT maps one inside address to one outside address.

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 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.

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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: Reduce temperature to 0.2, top_p to 0.5, and fine-tune the model on a curated dataset of 5,000 clinical summaries with a learning rate of 0.00005 and batch size of 8. — Option D is correct because reducing temperature and top_p makes the model more deterministic, reducing randomness and thus hallucinations. Fine-tuning on curated clinical data with a lower learning rate and smaller batch size aligns the model to the domain without excessive training. Option A might reduce hallucinations but increases latency and token cost. Option B only adds a safety filter, which does not address factual accuracy. Option C may change style but not reduce hallucinations.

What should I do if I get this 1Z0-1127 question wrong?

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 22, 2026

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