- A
Adjust the prediction threshold for the negative class
Why wrong: Adjusting the prediction threshold for the negative class can increase recall but will likely decrease precision, which violates the requirement of not sacrificing too much precision.
- B
Switch to a different model architecture (e.g., from BERT to RoBERTa)
Why wrong: Switching to a different model architecture like RoBERTa is a major change that may not specifically address the class imbalance causing low recall, and it introduces complexity without a clear guarantee.
- C
Collect more labeled examples of negative sentiment and retrain
Collecting more labeled examples of negative sentiment and retraining directly addresses the root cause (class imbalance or insufficient negative data), improving recall while maintaining precision.
- D
Use a larger pretrained model from Model Garden
Why wrong: Using a larger pretrained model from Model Garden may improve overall performance but does not specifically target the negative class recall issue, and it could be overkill.
Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
This Generative AI Leader practice question tests your understanding of google cloud's generative ai offerings. 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 uses Vertex AI Model Evaluation to assess a fine-tuned model for sentiment analysis. The evaluation report shows high precision but low recall on the 'negative' class. What is the best course of action to improve recall without sacrificing too much precision?
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
Collect more labeled examples of negative sentiment and retrain
Option C is correct because collecting more labeled examples of negative sentiment and retraining addresses the root cause of low recall: insufficient or imbalanced training data for the negative class. This improves the model's ability to recognize negative sentiment without sacrificing precision, as the decision boundary is refined with more representative data. Option A (adjusting prediction threshold) can increase recall but typically at the cost of precision, contradicting the goal. Option B (switching model architecture) is excessive and may not fix data imbalance. Option D (using a larger pretrained model) does not specifically target recall on the negative class.
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.
- ✗
Adjust the prediction threshold for the negative class
Why it's wrong here
Adjusting the prediction threshold for the negative class can increase recall but will likely decrease precision, which violates the requirement of not sacrificing too much precision.
- ✗
Switch to a different model architecture (e.g., from BERT to RoBERTa)
Why it's wrong here
Switching to a different model architecture like RoBERTa is a major change that may not specifically address the class imbalance causing low recall, and it introduces complexity without a clear guarantee.
- ✓
Collect more labeled examples of negative sentiment and retrain
Why this is correct
Collecting more labeled examples of negative sentiment and retraining directly addresses the root cause (class imbalance or insufficient negative data), improving recall while maintaining precision.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a larger pretrained model from Model Garden
Why it's wrong here
Using a larger pretrained model from Model Garden may improve overall performance but does not specifically target the negative class recall issue, and it could be overkill.
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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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Google Cloud's Generative AI Offerings — This question tests Google Cloud's Generative AI Offerings — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Collect more labeled examples of negative sentiment and retrain — Option C is correct because collecting more labeled examples of negative sentiment and retraining addresses the root cause of low recall: insufficient or imbalanced training data for the negative class. This improves the model's ability to recognize negative sentiment without sacrificing precision, as the decision boundary is refined with more representative data. Option A (adjusting prediction threshold) can increase recall but typically at the cost of precision, contradicting the goal. Option B (switching model architecture) is excessive and may not fix data imbalance. Option D (using a larger pretrained model) does not specifically target recall on the negative class.
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: Jun 23, 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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