- A
It automatically adjusts hyperparameters during inference.
Why wrong: Hyperparameters are set before training; T-Few does not adjust them during inference.
- B
It does not require any training data and works by prompting only.
Why wrong: T-Few requires a training dataset with prompt/completion pairs.
- C
It updates all model parameters, requiring substantial compute resources.
Why wrong: T-Few updates only a small subset of parameters (e.g., via adapter layers).
- D
It is a parameter-efficient fine-tuning method that updates only a fraction of the model parameters.
T-Few uses low-rank adaptations to efficiently fine-tune models.
Question 907 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.
Which statement accurately describes the T-Few fine-tuning technique used in OCI Generative AI?
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
✓
It is a parameter-efficient fine-tuning method that updates only a fraction of the model parameters.
The T-Few fine-tuning technique is a parameter-efficient fine-tuning (PEFT) method that updates only a small fraction of the model's parameters, typically by introducing and training adapter layers or using low-rank updates. This approach significantly reduces computational and memory requirements compared to full fine-tuning, making it suitable for adapting large language models with limited resources. In OCI Generative AI, T-Few enables efficient customization without retraining the entire model.
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.
- ✗
It automatically adjusts hyperparameters during inference.
Why it's wrong here
Hyperparameters are set before training; T-Few does not adjust them during inference.
- ✗
It does not require any training data and works by prompting only.
Why it's wrong here
T-Few requires a training dataset with prompt/completion pairs.
- ✗
It updates all model parameters, requiring substantial compute resources.
Why it's wrong here
T-Few updates only a small subset of parameters (e.g., via adapter layers).
- ✓
It is a parameter-efficient fine-tuning method that updates only a fraction of the model parameters.
Why this is correct
T-Few uses low-rank adaptations to efficiently fine-tune models.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse parameter-efficient fine-tuning (PEFT) with full fine-tuning or prompting, leading them to select options that describe full parameter updates or no training at all, rather than recognizing T-Few as a lightweight adaptation method.
Detailed technical explanation
How to think about this question
T-Few leverages adapter-based fine-tuning where small trainable modules are inserted into each transformer layer, while the original pre-trained weights remain frozen. This technique is inspired by methods like LoRA (Low-Rank Adaptation) and adapter layers, which enable task-specific adaptation with minimal parameter changes. In practice, T-Few can achieve performance comparable to full fine-tuning on many tasks while reducing memory usage by over 90%, making it ideal for deploying customized models in resource-constrained environments.
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
Got this wrong? Here's your next step.
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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: It is a parameter-efficient fine-tuning method that updates only a fraction of the model parameters. — The T-Few fine-tuning technique is a parameter-efficient fine-tuning (PEFT) method that updates only a small fraction of the model's parameters, typically by introducing and training adapter layers or using low-rank updates. This approach significantly reduces computational and memory requirements compared to full fine-tuning, making it suitable for adapting large language models with limited resources. In OCI Generative AI, T-Few enables efficient customization without retraining the entire model.
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
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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