AI0-001 AI Concepts and Techniques Practice Question
A company wants to generate realistic images of new product designs. They have a large dataset of existing product images. Which generative AI approach is MOST suitable for creating novel, high-quality images?
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
✓
Generative adversarial network (GAN)
GANs (Generative Adversarial Networks) consist of a generator and discriminator that compete, producing highly realistic images. Diffusion models are also good but GANs are historically the go-to for image generation. VAEs produce blurrier images; LLMs are for text.
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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Large language model (LLM)
Why it's wrong here
LLMs are optimized for text generation, not image synthesis.
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Variational autoencoder (VAE)
Why it's wrong here
Variational autoencoders (VAEs) optimise a variational lower bound on the log-likelihood, which forces a trade-off between reconstruction fidelity and latent-space regularity, often producing blurry outputs that lack the fine-grained detail required for realistic product images. This approach is tempting because VAEs excel at learning smooth, continuous latent representations for tasks like anomaly detection or controlled interpolation, where generating slightly varied versions of existing designs is the goal.
- ✓
Generative adversarial network (GAN)
Why this is correct
GANs are designed to generate high-quality, realistic images by adversarial training.
- ✗
Diffusion model
Why it's wrong here
Diffusion models are also effective for image generation, but GANs are more established for this specific task; however, both are plausible. But GAN is the classic answer.
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