AI0-001 AI Concepts and Techniques Practice Question
A company wants to build a system that can generate new product images for an online catalog. Which TWO generative AI approaches are most suitable?
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
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Diffusion models
Generative Adversarial Networks (GANs) are widely used for image generation, and diffusion models (like Stable Diffusion) have achieved state-of-the-art results in image synthesis. Variational autoencoders (VAEs) can generate images but often produce blurrier outputs. GPT is for text, and BERT is for understanding.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Diffusion models
Why this is correct
Diffusion models like DALL-E and Stable Diffusion produce high-quality images from noise.
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Variational autoencoders (VAEs)
Why it's wrong here
VAEs can generate images but often produce lower quality than GANs or diffusion models.
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Generative Adversarial Networks (GANs)
Why this is correct
GANs are effective for generating high-quality, realistic images.
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BERT-based model
Why it's wrong here
BERT is for natural language understanding, not generation.
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GPT-style language model
Why it's wrong here
GPT models are designed for text generation, not image generation.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.