- A
Few-shot learning
Few-shot learning provides examples in the prompt to adapt the model to a new task.
- B
Fine-tuning
Why wrong: Fine-tuning requires more data and computational resources.
- C
Pre-training
Why wrong: Pre-training trains from scratch on large data, not for few-shot adaptation.
- D
Prompt engineering
Why wrong: Prompt engineering is broader; few-shot is a subset but the question asks for a specific technique.
Question 156 of 991
1Z0-1127 Fundamentals of Large Language Models Practice Question
This 1Z0-1127 practice question tests your understanding of fundamentals of large language models. 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 technique allows an LLM to be adapted to a new task with only a few examples?
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
✓
Few-shot learning
Few-shot learning (A) is the correct technique because it enables an LLM to adapt to a new task by providing a small number of input-output examples directly in the prompt, without updating the model's weights. This leverages the model's in-context learning ability, where it generalizes from the provided examples to perform the task. It is distinct from fine-tuning, which requires retraining on a labeled dataset.
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.
- ✓
Few-shot learning
Why this is correct
Few-shot learning provides examples in the prompt to adapt the model to a new task.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Fine-tuning
Why it's wrong here
Fine-tuning requires more data and computational resources.
- ✗
Pre-training
Why it's wrong here
Pre-training trains from scratch on large data, not for few-shot adaptation.
- ✗
Prompt engineering
Why it's wrong here
Prompt engineering is broader; few-shot is a subset but the question asks for a specific technique.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle OCI GenAI often tests the distinction between few-shot learning and prompt engineering, where candidates mistakenly think that simply rephrasing the prompt (prompt engineering) is equivalent to providing examples (few-shot learning), but the key is the inclusion of explicit input-output pairs in the prompt.
Detailed technical explanation
How to think about this question
Few-shot learning relies on the transformer's attention mechanism to recognize patterns from the provided examples in the context window, often using a format like 'Q: [input] A: [output]' repeated for each example. A subtle behavior is that the order and quality of examples significantly impact performance, and the model may overfit to spurious correlations if examples are not diverse. In real-world scenarios, this is used for tasks like sentiment classification or entity extraction without retraining, but the context window size (e.g., 4K or 8K tokens) limits the number of examples that can be provided.
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?
Fundamentals of Large Language Models — This question tests Fundamentals of Large Language Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Few-shot learning — Few-shot learning (A) is the correct technique because it enables an LLM to adapt to a new task by providing a small number of input-output examples directly in the prompt, without updating the model's weights. This leverages the model's in-context learning ability, where it generalizes from the provided examples to perform the task. It is distinct from fine-tuning, which requires retraining on a labeled dataset.
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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