Generative AI Leader Fundamentals of Generative AI Practice Question
A company is fine-tuning a Gemma model using Vertex AI. They observe that the model overfits. Which TWO actions should they take to mitigate overfitting?
⚠ Common exam trap
Google Cloud often tests the misconception that reducing the learning rate or increasing batch size are universal fixes for overfitting, when in fact these hyperparameters primarily affect optimization dynamics rather than regularization.
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
✓
Use more diverse data
Introducing more diverse data helps the model generalize better by exposing it to a wider variety of patterns, reducing the risk of memorizing noise from a limited dataset. Option E is correct because dropout randomly deactivates a fraction of neurons during fine-tuning, which prevents co-adaptation and acts as a regularization technique to combat overfitting in transformer-based models like Gemma.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a larger batch size
Why it's wrong here
Larger batch size can provide more stable gradients but doesn't prevent overfitting.
- ✗
Increase the number of training epochs
Why it's wrong here
More epochs typically increase overfitting.
- ✓
Use more diverse data
Why this is correct
More diverse training data reduces overfitting to narrow patterns.
- ✗
Reduce the learning rate
Why it's wrong here
Lower learning rate slows learning but does not directly address overfitting.
- ✓
Add dropout during fine-tuning
Why this is correct
Dropout is a regularization technique that helps prevent overfitting.
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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.