Generative AI Leader Fundamentals of Generative AI Practice Question
Which TWO of the following are key differences between generative AI and discriminative AI? (Choose two.)
⚠ Common exam trap
Google Cloud often tests the misconception that generative models are only for unsupervised tasks and cannot perform classification, leading candidates to incorrectly select Option C, while also testing the false assumption that discriminative models are universally superior, as in Option E.
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 models can create new data samples, while discriminative models only assign labels to existing data.
Generative AI models learn the underlying distribution of the data, enabling them to generate new, realistic samples (e.g., images, text) from the learned distribution. In contrast, discriminative models learn decision boundaries to classify or label existing data without the ability to create new data instances. This fundamental difference in capability—creation versus discrimination—is a core distinction between the two paradigms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Generative models can create new data samples, while discriminative models only assign labels to existing data.
Why this is correct
Generation is a hallmark of generative AI.
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Generative models require less training data than discriminative models.
Why it's wrong here
Generative models often require more data to capture the full distribution.
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Generative models cannot be used for supervised learning tasks like classification.
Why it's wrong here
Generative models can be used for classification by computing P(Y|X) via Bayes rule.
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Generative models model the joint probability distribution of inputs and labels, whereas discriminative models model the conditional probability of labels given inputs.
Why this is correct
This is a fundamental theoretical distinction.
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Discriminative models always outperform generative models on tasks like image classification.
Why it's wrong here
Performance varies; generative models can sometimes excel.
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