- A
Prompt engineer a large foundation model with few-shot examples in Vertex AI Studio
Why wrong: Few-shot prompting may not be consistent enough for a strict classification taxonomy; performance can vary.
- B
Use the RAG Engine to retrieve similar clauses and ask the model to classify
Why wrong: RAG is designed for generation with retrieval, not for classification tasks; it adds complexity without improving classification accuracy.
- C
Fine-tune a base model using the labeled examples in Vertex AI
Fine-tuning adapts the model to the specific classification task and taxonomy, achieving higher accuracy with limited examples.
- D
Use a larger foundation model without fine-tuning and rely on its pre-trained knowledge
Why wrong: General pre-trained knowledge may not align with the company's specific taxonomy and can produce inconsistent classifications.
Generative AI Leader Applying Generative AI in Business Practice Question
This Generative AI Leader practice question tests your understanding of applying generative ai in business. 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 legal team wants to use GenAI to review contracts and highlight risky clauses. They need the AI to consistently follow a specific classification taxonomy. The team has a small set of labeled examples (500 contracts). Which approach yields the BEST accuracy for this use case?
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
Fine-tune a base model using the labeled examples in Vertex AI
Fine-tuning a base model on the labeled examples teaches the model the specific taxonomy with high accuracy, even with a small dataset. Prompt engineering alone may be inconsistent. Larger model without fine-tuning may not adhere to taxonomy. RAG is for retrieval, not classification.
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.
- ✗
Prompt engineer a large foundation model with few-shot examples in Vertex AI Studio
Why it's wrong here
Few-shot prompting may not be consistent enough for a strict classification taxonomy; performance can vary.
- ✗
Use the RAG Engine to retrieve similar clauses and ask the model to classify
Why it's wrong here
RAG is designed for generation with retrieval, not for classification tasks; it adds complexity without improving classification accuracy.
- ✓
Fine-tune a base model using the labeled examples in Vertex AI
Why this is correct
Fine-tuning adapts the model to the specific classification task and taxonomy, achieving higher accuracy with limited examples.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a larger foundation model without fine-tuning and rely on its pre-trained knowledge
Why it's wrong here
General pre-trained knowledge may not align with the company's specific taxonomy and can produce inconsistent classifications.
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 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.
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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Applying Generative AI in Business — study guide chapter
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Applying Generative AI in Business — This question tests Applying Generative AI in Business — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Fine-tune a base model using the labeled examples in Vertex AI — Fine-tuning a base model on the labeled examples teaches the model the specific taxonomy with high accuracy, even with a small dataset. Prompt engineering alone may be inconsistent. Larger model without fine-tuning may not adhere to taxonomy. RAG is for retrieval, not classification.
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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