- A
Reinforcement Learning from Human Feedback (RLHF)
Why wrong: RLHF requires a reward model and human feedback, which is complex and not necessary for summarization.
- B
In-context learning with few-shot examples in prompts
Why wrong: In-context learning does not update model weights and may not perform well for specialized tasks with limited examples.
- C
Supervised fine-tuning using LoRA adapters
LoRA adapters are parameter-efficient, reducing risk of forgetting and requiring less data.
- D
Full fine-tuning of all model parameters
Why wrong: Full fine-tuning on limited data risks overfitting and catastrophic forgetting.
Question 273 of 997
Generative AI Leader Generative AI Concepts and Technologies Practice Question
This Generative AI Leader practice question tests your understanding of generative ai concepts and technologies. 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 company is fine-tuning a large language model for a domain-specific legal document summarization task. They have limited labeled data but want to adapt the model efficiently without catastrophic forgetting. Which technique is most suitable?
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
✓
Supervised fine-tuning using LoRA adapters
LoRA (Low-Rank Adaptation) is the most suitable technique because it enables parameter-efficient fine-tuning by injecting trainable low-rank matrices into the transformer layers, drastically reducing the number of updated parameters. This preserves the pre-trained knowledge and prevents catastrophic forgetting, even with limited labeled data, while efficiently adapting the model to domain-specific legal summarization.
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.
- ✗
Reinforcement Learning from Human Feedback (RLHF)
Why it's wrong here
RLHF requires a reward model and human feedback, which is complex and not necessary for summarization.
- ✗
In-context learning with few-shot examples in prompts
Why it's wrong here
In-context learning does not update model weights and may not perform well for specialized tasks with limited examples.
- ✓
Supervised fine-tuning using LoRA adapters
Why this is correct
LoRA adapters are parameter-efficient, reducing risk of forgetting and requiring less data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Full fine-tuning of all model parameters
Why it's wrong here
Full fine-tuning on limited data risks overfitting and catastrophic forgetting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common trap is the misconception that in-context learning (Option B) is sufficient for domain adaptation, but it does not modify model weights and thus cannot achieve the deep, consistent specialization required for tasks like legal document summarization.
Detailed technical explanation
How to think about this question
LoRA works by decomposing the weight update matrix ΔW into two low-rank matrices A and B (rank r << d), applied to attention projection layers, so only these small matrices are trained while the original weights remain frozen. This reduces trainable parameters by up to 10,000x, enabling fine-tuning on a single GPU with as few as 100–1,000 examples. In practice, for legal summarization, LoRA adapters can be swapped or merged without affecting the base model, allowing rapid iteration across multiple legal sub-domains.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Generative AI Concepts and Technologies — This question tests Generative AI Concepts and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Supervised fine-tuning using LoRA adapters — LoRA (Low-Rank Adaptation) is the most suitable technique because it enables parameter-efficient fine-tuning by injecting trainable low-rank matrices into the transformer layers, drastically reducing the number of updated parameters. This preserves the pre-trained knowledge and prevents catastrophic forgetting, even with limited labeled data, while efficiently adapting the model to domain-specific legal summarization.
What should I do if I get this Generative AI Leader 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
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