Generative AI Leader Fundamentals of Generative AI Practice Question
You are the AI lead at an e-commerce company that uses a generative model to write product descriptions from images and key attributes. The model is a multimodal transformer that encodes both image and text (attributes) and decodes a description. Recently, your team deployed a new version of the image encoder that uses a more powerful backbone (ViT-L instead of ViT-B). After deployment, the generated descriptions became longer but often include irrelevant visual details (e.g., background objects) and occasionally misrepresent the product's main features. The model was fine-tuned on the same dataset as before. The descriptions from the old model were concise and focused. What is the most likely cause of the degradation and the best fix?
⚠ Common exam trap
Google Cloud often tests the misconception that a more powerful encoder always improves performance, when in fact it can introduce overfitting to irrelevant features, and the fix is not to downgrade the encoder but to add regularization that guides attention to salient regions.
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
✓
The powerful encoder introduces overfitting to the training images; continue fine-tuning with additional loss terms that penalize description of irrelevant details (e.g., using attention regularization).
The more powerful ViT-L encoder captures richer, high-resolution features, including background details, which the decoder then amplifies into longer, less focused descriptions. This is a form of overfitting to irrelevant visual patterns in the training images, not to the labels. Adding attention regularization (e.g., penalizing attention weights on non-salient regions) forces the model to focus on product-relevant features, restoring conciseness and accuracy without reverting to the weaker encoder.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The decoder is now too small relative to the encoder; reduce the encoder's hidden size or increase the decoder's capacity.
Why it's wrong here
Mismatched sizes can cause issues but more likely the encoder is providing noisy signals.
- ✗
The new encoder produces less discriminative features; replace it with an older version.
Why it's wrong here
The new encoder likely produces richer but less targeted features; a better fix is to guide it.
- ✗
Lower the decoder's temperature to reduce diversity and hallucination.
Why it's wrong here
Lower temperature does not solve the problem of encoder providing irrelevant information.
- ✓
The powerful encoder introduces overfitting to the training images; continue fine-tuning with additional loss terms that penalize description of irrelevant details (e.g., using attention regularization).
Why this is correct
Attention regularization forces the model to focus on product-relevant regions.
Go deeper
Related to this question
About these practice questions
This Generative AI Leader question is part of Courseiva's 683-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.