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 inflates evaluation metrics; it does not degrade the original task. It tempts because leakage is genuinely the cause when validation scores look implausibly high, whereas the described drop on the original task points to catastrophic forgetting during fine-tuning.
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Model quantization
Why it's wrong here
Quantization reduces numerical precision, costing some accuracy, but it does not selectively erase the original task while a new one is learned. It tempts because quantization is genuinely the cause when a deployed model loses accuracy after compression for latency or memory savings.
- ✓
Catastrophic forgetting
Why this is correct
Catastrophic forgetting occurs when gradient updates for the new summarisation task overwrite the weights encoding the original language-understanding capability. This directly explains the performance drop on the earlier task, satisfying the stem's observation of degraded prior-task accuracy after fine-tuning.
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Underfitting
Why it's wrong here
Underfitting means the model fails to learn the training data at all, producing poor results everywhere, not a drop on the original task after new training. It is tempting because both involve weak performance, but underfitting is diagnosed from high training loss, whereas this scenario describes catastrophic forgetting during fine-tuning.
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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.