- A
Gather additional toxic comments from similar platforms to augment the training data.
Why wrong: More data helps both recall and precision but is time-consuming; the priority is precision improvement.
- B
Apply a higher weight to the toxic class in the loss function during fine-tuning.
Why wrong: Class weighting often increases recall but may further reduce precision.
- C
Use a smaller pre-trained model that is inherently less sensitive to subtle toxic language.
Why wrong: Smaller models often have lower recall and may not improve precision.
- D
Tune the classification threshold on a held-out validation set to a higher value (e.g., require higher probability to classify as toxic).
Increasing the threshold reduces false positives (improves precision) with some loss in recall, which can be fine-tuned.
Generative AI Leader Fundamentals of Generative AI Practice Question
This Generative AI Leader practice question tests your understanding of fundamentals of generative ai. 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.
You are a generative AI architect at a social media company. You are tasked with building a content moderation system that uses a generative model to flag toxic comments. The system must have very low false positive rates (i.e., not flag harmless comments) to avoid user backlash, but it must catch nearly all toxic comments. You have a large dataset of labeled toxic and non-toxic comments. You plan to use a pre-trained LLM and fine-tune it for classification. During experimentation, you notice that the model's recall for toxic comments is high (95%) but its precision is low (60%), leading to many false positives. You need to improve precision without substantially reducing recall. Which approach should you try first?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
Tune the classification threshold on a held-out validation set to a higher value (e.g., require higher probability to classify as toxic).
Option D is correct because threshold tuning is a straightforward, post-hoc method to trade recall for precision by raising the decision threshold. Option A is incorrect because adding more toxic samples might increase recall but not necessarily precision. Option B is incorrect because a smaller model might have less capacity to distinguish nuance, worsening precision. Option C is incorrect because class weighting can improve recall but may hurt precision.
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.
- ✗
Gather additional toxic comments from similar platforms to augment the training data.
Why it's wrong here
More data helps both recall and precision but is time-consuming; the priority is precision improvement.
- ✗
Apply a higher weight to the toxic class in the loss function during fine-tuning.
Why it's wrong here
Class weighting often increases recall but may further reduce precision.
- ✗
Use a smaller pre-trained model that is inherently less sensitive to subtle toxic language.
Why it's wrong here
Smaller models often have lower recall and may not improve precision.
- ✓
Tune the classification threshold on a held-out validation set to a higher value (e.g., require higher probability to classify as toxic).
Why this is correct
Increasing the threshold reduces false positives (improves precision) with some loss in recall, which can be fine-tuned.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
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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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Tune the classification threshold on a held-out validation set to a higher value (e.g., require higher probability to classify as toxic). — Option D is correct because threshold tuning is a straightforward, post-hoc method to trade recall for precision by raising the decision threshold. Option A is incorrect because adding more toxic samples might increase recall but not necessarily precision. Option B is incorrect because a smaller model might have less capacity to distinguish nuance, worsening precision. Option C is incorrect because class weighting can improve recall but may hurt precision.
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.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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: 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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