- A
It requires unlabeled data for unsupervised pre-training before fine-tuning
Why wrong: T-Few is a supervised fine-tuning technique that uses labeled prompt/completion pairs, not unlabeled data.
- B
It updates all model parameters, requiring significant compute resources
Why wrong: T-Few is parameter-efficient and only updates a small subset of parameters.
- C
It modifies a small subset of parameters using lightweight adapter layers
T-Few uses adapter-based fine-tuning that updates a small number of parameters while keeping most of the model frozen.
- D
It only trains a new classification head on top of a frozen base model
Why wrong: T-Few updates internal parameters via adapter-like transformations, not just a classification head.
Question 276 of 991
T-Few Fine-Tuning in OCI Generative AI: Technique, Data Format, and Dataset Size Requirements
This 1Z0-1127 practice question tests your understanding of oci generative ai service. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 data scientist needs to fine-tune a model using OCI Generative AI. They have prepared a dataset in JSONL format with prompt/completion pairs. The fine-tuning job is configured with the T-Few technique. What is a key characteristic of T-Few fine-tuning?
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 modifies a small subset of parameters using lightweight adapter layers
T-Few is a parameter-efficient fine-tuning method that updates only a small fraction of model parameters, making it faster and more resource-efficient than full fine-tuning. It does not train all parameters, add new layers, or require unlabeled data.
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 requires unlabeled data for unsupervised pre-training before fine-tuning
Why it's wrong here
T-Few is a supervised fine-tuning technique that uses labeled prompt/completion pairs, not unlabeled data.
- ✗
It updates all model parameters, requiring significant compute resources
Why it's wrong here
T-Few is parameter-efficient and only updates a small subset of parameters.
- ✓
It modifies a small subset of parameters using lightweight adapter layers
Why this is correct
T-Few uses adapter-based fine-tuning that updates a small number of parameters while keeping most of the model frozen.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
It only trains a new classification head on top of a frozen base model
Why it's wrong here
T-Few updates internal parameters via adapter-like transformations, not just a classification head.
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.
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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 modifies a small subset of parameters using lightweight adapter layers — T-Few is a parameter-efficient fine-tuning method that updates only a small fraction of model parameters, making it faster and more resource-efficient than full fine-tuning. It does not train all parameters, add new layers, or require unlabeled data.
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.
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 →
Same concept, more angles
5 more ways this is tested on 1Z0-1127
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is fine-tuning a model using T-Few in OCI Generative AI. They have prepared a dataset with prompt/completion pairs. Which file format is required for the training data upload?
medium- A.Plain text file with one prompt-completion pair per line separated by a tab
- B.Parquet file with a 'text' column containing concatenated prompt and completion
- C.CSV with columns 'input' and 'output'
- ✓ D.JSONL with each line containing 'prompt' and 'completion' keys
Why D: OCI Generative AI fine-tuning expects training data in JSONL format with specific fields.
Variation 2. A data scientist needs to fine-tune a large language model on a custom dataset of 10,000 prompt-completion pairs. They want to minimize cost while still updating the model effectively. Which fine-tuning technique is used by OCI Generative AI service?
medium- A.Prefix tuning
- ✓ B.T-Few fine-tuning
- C.Adapter fine-tuning
- D.LoRA fine-tuning
Why B: OCI Generative AI uses T-Few, which updates only a small number of parameters via learned transformations, reducing computational cost while maintaining performance. Adapter, LoRA, and prefix tuning are general PEFT methods but not the specific technique offered by OCI.
Variation 3. A data scientist needs to fine-tune a Llama 3 model for a legal document classification task. They have a dataset of 10,000 labeled examples. Which fine-tuning technique available in OCI Generative AI is most suitable for efficiently adapting the model with limited computational overhead?
medium- A.Full fine-tuning all model parameters
- B.LoRA (Low-Rank Adaptation)
- C.Prefix tuning
- ✓ D.T-Few fine-tuning
Why D: T-Few fine-tuning is a parameter-efficient technique that updates only a small number of weights, making it suitable for fine-tuning large models with limited compute. It is the technique offered by OCI GenAI for fine-tuning.
Variation 4. A machine learning engineer is preparing a dataset for fine-tuning a model in OCI Generative AI. The dataset consists of customer support conversations with questions and desired answers. What is the required format for the training data?
medium- A.CSV file with columns 'input' and 'output'
- B.Parquet file with 'text' and 'label' columns
- C.Plain text file with examples separated by blank lines
- ✓ D.JSONL file with 'prompt' and 'completion' fields per line
Why D: OCI Generative AI expects a JSONL file where each line is a JSON object with 'prompt' and 'completion' fields. CSV, Parquet, and text files are not supported for fine-tuning.
Variation 5. During fine-tuning, a user notices the loss does not decrease after several epochs. The dataset is a JSONL file with 500 prompt/completion pairs. What is the MOST likely cause?
hard- A.The JSONL format is incorrect because it lacks system prompts
- B.The base model is not compatible with the T-Few technique
- ✓ C.The dataset is too small; T-Few fine-tuning generally needs at least 1000 examples
- D.The learning rate is too high, causing the model to diverge
Why C: Fine-tuning with T-Few typically requires at least 1000 examples for meaningful learning. The dataset size is likely insufficient.
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Last reviewed: Jul 4, 2026
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