- 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.
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?
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, directly reducing the likelihood of hallucinated content. Fine-tuning on a curated dataset of 5,000 clinical summaries teaches the model domain-specific patterns and constraints, further minimizing hallucinations. This approach addresses the root cause without significantly increasing latency, as fine-tuning does not affect inference speed and lower sampling parameters add no computational overhead.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle OCI GenAI exam often tests the misconception that adding more context or post-processing steps can fix hallucinations, when in fact controlling model parameters and fine-tuning are the primary methods to reduce fabricated content in generative AI.
Detailed technical explanation
How to think about this question
Temperature controls the randomness of token sampling; lower values (e.g., 0.2) make the model more deterministic and less likely to generate improbable sequences. Top_p (nucleus sampling) limits the cumulative probability of token choices; reducing it to 0.5 further restricts the model to high-probability tokens. Fine-tuning adjusts model weights using a domain-specific dataset, aligning the model's output distribution with clinical ground truth, which is more effective than prompt engineering alone for reducing hallucinations.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the 1Z0-1127 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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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 — Read the scenario before looking for a memorised answer..
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, directly reducing the likelihood of hallucinated content. Fine-tuning on a curated dataset of 5,000 clinical summaries teaches the model domain-specific patterns and constraints, further minimizing hallucinations. This approach addresses the root cause without significantly increasing latency, as fine-tuning does not affect inference speed and lower sampling parameters add no computational overhead.
What should I do if I get this 1Z0-1127 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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