- A
Few-shot learning
In-context learning with a few examples steers the model without retraining.
- B
Reinforcement Learning from Human Feedback (RLHF)
Why wrong: RLHF is a training technique, not in-context learning.
- C
Prompt engineering
Why wrong: Prompt engineering includes few-shot, but the specific term is few-shot learning.
- D
Fine-tuning
Why wrong: Fine-tuning retrains the model on additional data.
Question 425 of 991
OCI Generative AI Few-Shot Learning
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. A key principle to apply: few-shot learning. 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 OCI Generative AI capability allows you to provide example input-output pairs to guide the model's behavior without 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
✓
Few-shot learning
Few-shot learning is the correct answer because it allows you to provide a small number of example input-output pairs directly in the prompt to guide the model's behavior for a specific task, without updating the model's weights through fine-tuning. In OCI Generative AI, this technique leverages the model's in-context learning ability to adapt its responses based on the provided examples, making it ideal for quick task adaptation without the cost or complexity of retraining.
Key principle: Few-shot learning
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
In-context learning with a few examples steers the model without retraining.
Related concept
Few-shot learning
- ✗
Reinforcement Learning from Human Feedback (RLHF)
Why it's wrong here
RLHF is a training technique, not in-context learning.
- ✗
Prompt engineering
Why it's wrong here
Prompt engineering includes few-shot, but the specific term is few-shot learning.
- ✗
Fine-tuning
Why it's wrong here
Fine-tuning retrains the model on additional data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Oracle OCI often tests the distinction between prompt-level techniques (few-shot learning, prompt engineering) and training-level techniques (RLHF, fine-tuning). The trap here is that candidates may confuse 'providing examples' with 'prompt engineering' or mistakenly think RLHF is a prompt-based method, when in fact RLHF is a post-training alignment process that modifies model weights.
Detailed technical explanation
How to think about this question
Few-shot learning in OCI Generative AI works by including a few complete examples (e.g., 'Input: translate 'hello' to French -> Output: bonjour') within the prompt, which the model uses as a pattern to infer the desired output format and logic for subsequent inputs. This leverages the transformer architecture's attention mechanism, where the model attends to the provided examples as context, effectively performing in-context learning without gradient updates. A subtle behavior is that the number and quality of examples significantly impact performance; too few may lead to ambiguity, while too many can exceed the model's context window (e.g., 4,096 tokens for some models), causing truncation or degraded accuracy.
KKey Concepts to Remember
- Few-shot learning
- In-context learning
- Prompt engineering
- Fine-tuning
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
Few-shot learning
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. Few-shot learning 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.
Review few-shot learning, then practise related 1Z0-1127 questions on the same topic to reinforce the concept.
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Using OCI Generative AI Service — study guide chapter
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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 — Few-shot learning.
What is the correct answer to this question?
The correct answer is: Few-shot learning — Few-shot learning is the correct answer because it allows you to provide a small number of example input-output pairs directly in the prompt to guide the model's behavior for a specific task, without updating the model's weights through fine-tuning. In OCI Generative AI, this technique leverages the model's in-context learning ability to adapt its responses based on the provided examples, making it ideal for quick task adaptation without the cost or complexity of retraining.
What should I do if I get this 1Z0-1127 question wrong?
Review few-shot learning, then practise related 1Z0-1127 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Few-shot learning
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Last reviewed: Jul 4, 2026
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