- A
Fine-tune the model using T-Few technique within OCI
T-Few fine-tuning runs inside OCI, ensuring data does not leave the tenancy.
- B
Use OCI GenAI on-demand inference for the fine-tuned model
Why wrong: On-demand inference uses shared infrastructure, violating isolation and latency requirements.
- C
Ensure the model is deployed on the dedicated cluster after fine-tuning
Deploying the fine-tuned model on the dedicated cluster meets isolation and latency goals.
- D
Provision a dedicated AI cluster with model units
Dedicated cluster ensures isolation and low-latency inference.
- E
Host the model on a shared AI cluster to reduce cost
Why wrong: Shared cluster does not provide isolation and may have variable latency.
Question 640 of 991
1Z0-1127 OCI Generative AI Service Practice Question
This 1Z0-1127 practice question tests your understanding of 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 financial services company must deploy a fine-tuned model for transaction categorization. The model must be isolated from other tenants and provide predictable low-latency inference. The compliance team also requires that training data never leaves the OCI tenancy. Which THREE steps should the team take? (Choose three.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"never"Why it matters: Absolute qualifier. True only if the statement has zero exceptions — be cautious of options that seem obvious but break down in edge cases.
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
✓
Fine-tune the model using T-Few technique within OCI
A dedicated AI cluster provides isolation and predictable low latency. T-Few fine-tuning runs entirely within OCI, keeping data in tenancy. Model units are required for dedicated cluster provisioning. Shared infrastructure would compromise isolation. On-demand inference does not guarantee low latency.
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.
- ✓
Fine-tune the model using T-Few technique within OCI
Why this is correct
T-Few fine-tuning runs inside OCI, ensuring data does not leave the tenancy.
Clue confirmation
The clue word "never" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use OCI GenAI on-demand inference for the fine-tuned model
Why it's wrong here
On-demand inference uses shared infrastructure, violating isolation and latency requirements.
- ✓
Ensure the model is deployed on the dedicated cluster after fine-tuning
Why this is correct
Deploying the fine-tuned model on the dedicated cluster meets isolation and latency goals.
Clue confirmation
The clue word "never" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Provision a dedicated AI cluster with model units
Why this is correct
Dedicated cluster ensures isolation and low-latency inference.
Clue confirmation
The clue word "never" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Host the model on a shared AI cluster to reduce cost
Why it's wrong here
Shared cluster does not provide isolation and may have variable latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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
Got this wrong? Here's your next step.
Identify which 1Z0-1127 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this 1Z0-1127 question test?
OCI Generative AI Service — This question tests 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: Fine-tune the model using T-Few technique within OCI — A dedicated AI cluster provides isolation and predictable low latency. T-Few fine-tuning runs entirely within OCI, keeping data in tenancy. Model units are required for dedicated cluster provisioning. Shared infrastructure would compromise isolation. On-demand inference does not guarantee low latency.
What should I do if I get this 1Z0-1127 question wrong?
Identify which 1Z0-1127 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "never". Absolute qualifier. True only if the statement has zero exceptions — be cautious of options that seem obvious but break down in edge cases.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 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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