Generative AI Leader Fundamentals of Generative AI Practice Question
A company fine-tunes a model using Vertex AI and notices the model's performance drops on the original training task (e.g., language understanding) after fine-tuning for a new task (e.g., summarization). What could be the cause?
⚠ Common exam trap
Google Cloud often tests the distinction between catastrophic forgetting and underfitting, as candidates may mistakenly think the model simply didn't learn the new task well, rather than recognizing that it forgot the original task due to weight overwriting.
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
✓
Catastrophic forgetting
Catastrophic forgetting occurs when a neural network loses previously learned knowledge upon being fine-tuned on a new task. In this scenario, fine-tuning the model for summarization overwrites the weights responsible for language understanding, causing performance degradation on the original task. This is a well-known limitation of sequential fine-tuning in deep learning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data leakage
Why it's wrong here
Data leakage might cause overoptimistic evaluation, not a distinct performance drop on the original task.
- ✗
Model quantization
Why it's wrong here
Quantization reduces model size but typically does not cause forgetting of original tasks.
- ✓
Catastrophic forgetting
Why this is correct
Fine-tuning on a narrow task can overwrite general knowledge, leading to performance degradation on the original task.
- ✗
Underfitting
Why it's wrong here
Underfitting would show poor performance on both tasks, not a drop on the original task alone.
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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.