- A
Switch to a larger base model and use zero-shot prompting
Why wrong: Zero-shot prompting on a larger model may not match the fine-tuned accuracy and could be more expensive per token.
- B
Retrain the model from scratch every quarter with all historical data
Why wrong: Full retraining from scratch is expensive and time-consuming; not necessary for minor language shifts.
- C
Perform incremental fine-tuning with a small representative sample of new contracts
Incremental fine-tuning updates the model on new patterns efficiently, using far less compute than full retraining.
- D
Use Vertex AI Model Monitoring to detect drift and alert, then manually adjust prompts
Why wrong: Manual prompt adjustment is not a systematic way to maintain model accuracy; it does not adapt the model's weights.
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 financial services firm uses a fine-tuned model for contract analysis. They observe that the model's performance degrades after a few months because contract language evolves. The team wants to maintain accuracy without full retraining. What is the MOST cost-effective approach?
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
Perform incremental fine-tuning with a small representative sample of new contracts
Fine-tuning the existing model with a small amount of new data (incremental fine-tuning) is the most cost-effective way to adapt to language evolution without full retraining. The other options are either too costly or do not adapt the model.
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.
- ✗
Switch to a larger base model and use zero-shot prompting
Why it's wrong here
Zero-shot prompting on a larger model may not match the fine-tuned accuracy and could be more expensive per token.
- ✗
Retrain the model from scratch every quarter with all historical data
Why it's wrong here
Full retraining from scratch is expensive and time-consuming; not necessary for minor language shifts.
- ✓
Perform incremental fine-tuning with a small representative sample of new contracts
Why this is correct
Incremental fine-tuning updates the model on new patterns efficiently, using far less compute than full retraining.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Vertex AI Model Monitoring to detect drift and alert, then manually adjust prompts
Why it's wrong here
Manual prompt adjustment is not a systematic way to maintain model accuracy; it does not adapt the model's weights.
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.
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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: Perform incremental fine-tuning with a small representative sample of new contracts — Fine-tuning the existing model with a small amount of new data (incremental fine-tuning) is the most cost-effective way to adapt to language evolution without full retraining. The other options are either too costly or do not adapt the model.
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 →
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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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