Generative AI Leader Fundamentals of Generative AI Practice Question
You are a data scientist at a financial institution. You are using Vertex AI to fine-tune a large language model (LLM) for generating financial reports. You have prepared a dataset of 10,000 examples. During fine-tuning, you notice that the training loss is decreasing steadily, but the validation loss is increasing after 5 epochs. The model's generated reports on the validation set contain many factual errors and nonsensical statements. You suspect overfitting. You have limited compute budget and need to improve generalization. What should you do?
⚠ Common exam trap
It's easy for candidates to confuse overfitting with underfitting and choose to add more data or increase epochs, failing to recognize that the validation loss increasing while training loss decreases is the classic sign of overfitting, which requires a regularization technique like early stopping.
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
✓
Implement early stopping with a patience of 2 epochs
Early stopping with a patience of 2 epochs is the correct approach because it directly addresses overfitting by halting training when the validation loss fails to improve for a specified number of epochs. This preserves the model's generalization ability without requiring additional compute or data, which aligns with the limited budget constraint. In Vertex AI, early stopping is a built-in hyperparameter tuning strategy that monitors validation metrics and stops the job to prevent further degradation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the learning rate
Why it's wrong here
Higher learning rate may not improve generalization.
- ✗
Increase the number of training epochs to 20
Why it's wrong here
More epochs increase overfitting.
- ✗
Add more training examples from a public dataset
Why it's wrong here
Budget is limited; public data may not be relevant.
- ✓
Implement early stopping with a patience of 2 epochs
Why this is correct
Early stopping prevents overfitting.
Go deeper
Related to this question
About these practice questions
Courseiva writes every Generative AI Leader question from scratch — 683 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.