- A
Full fine-tuning of all model parameters
Why wrong: Full fine-tuning is expensive, especially for large models, and can overfit on small datasets.
- B
Adapter-based fine-tuning (e.g., LoRA)
LoRA updates low-rank matrices, preserving the base model and reducing memory/storage requirements while adapting to the new task.
- C
Training a small custom model from scratch
Why wrong: Training from scratch requires much more data and compute; it is not cost-effective with only 200 examples.
- D
Prompt engineering with few-shot examples only
Why wrong: Prompt engineering may not capture the specialized legal style reliably with only 200 examples; fine-tuning is more effective.
Question 742 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 data scientist is using Vertex AI to fine-tune a Gemini model for a specialized legal document summarization task. They have a small set of labeled examples (200 pairs). Which fine-tuning method is MOST cost-effective and likely to perform well?
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
✓
Adapter-based fine-tuning (e.g., LoRA)
Adapter-based fine-tuning (like LoRA) updates only a small fraction of parameters, making it efficient with small datasets and low cost, while still adapting the model to the task.
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.
- ✗
Full fine-tuning of all model parameters
Why it's wrong here
Full fine-tuning is expensive, especially for large models, and can overfit on small datasets.
- ✓
Adapter-based fine-tuning (e.g., LoRA)
Why this is correct
LoRA updates low-rank matrices, preserving the base model and reducing memory/storage requirements while adapting to the new task.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Training a small custom model from scratch
Why it's wrong here
Training from scratch requires much more data and compute; it is not cost-effective with only 200 examples.
- ✗
Prompt engineering with few-shot examples only
Why it's wrong here
Prompt engineering may not capture the specialized legal style reliably with only 200 examples; fine-tuning is more effective.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which Generative AI Leader 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.
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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: Adapter-based fine-tuning (e.g., LoRA) — Adapter-based fine-tuning (like LoRA) updates only a small fraction of parameters, making it efficient with small datasets and low cost, while still adapting the model to the task.
What should I do if I get this Generative AI Leader question wrong?
Identify which Generative AI Leader 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 →
Last reviewed: Jul 4, 2026
This Generative AI Leader practice question is part of Courseiva's free Google Cloud 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 Generative AI Leader exam.
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