Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A media company is using Vertex AI Imagen to generate marketing images. The output frequently contains unrealistic artifacts, especially in human faces. The team has fine-tuned the model using their brand assets. What is the most likely cause and recommended fix?
⚠ Common exam trap
Test-takers frequently confuse inference parameters (like steps or safety filters) with data quality issues, assuming artifacts are due to model settings rather than the fundamental cause of insufficient or non-diverse training data.
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 fine-tuning dataset is too small or too homogeneous; augment and diversify the training data.
Unrealistic artifacts in fine-tuned generative models, especially in human faces, typically stem from a training dataset that is too small or lacks diversity. When the dataset is homogeneous, the model overfits to limited patterns and fails to generalize, leading to distorted outputs. Augmenting and diversifying the training data with varied poses, lighting, and ethnicities helps the model learn robust facial features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Safety filters are too aggressive; reduce them.
Why it's wrong here
Safety filters block policy-violating content and never generate anatomical artefacts; disabling them risks harmful output without improving faces. Reducing filters is correct only when legitimate prompts are wrongly refused, not when image quality is poor.
- ✗
Negative prompts are missing; always include 'unrealistic'.
Why it's wrong here
Negative prompts steer away from unwanted content but cannot repair facial anatomy corrupted by overfitting during fine-tuning on a narrow brand dataset. Negative prompts are correct when excluding specific unwanted elements, not fixing structural artefacts.
- ✓
The fine-tuning dataset is too small or too homogeneous; augment and diversify the training data.
Why this is correct
Fine-tuning on a narrow, homogeneous brand dataset biases the model toward those patterns, degrading general facial structure and producing artefacts. Augmenting with diverse, larger image sets restores the visual distribution the model needs, correcting the unrealistic faces while retaining brand style.
- ✗
Inference steps are too low; increase to 100.
Why it's wrong here
Diffusion inference steps control denoising iterations; low counts cause blur or noise, not the facial distortion that fine-tuning on limited brand assets induces. Raising steps is correct when output looks under-rendered rather than anatomically wrong.
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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.