An architect is explaining to a stakeholder how a generative foundation model differs from a traditional discriminative classifier that outputs a single label. Which two statements accurately capture these concepts? Select TWO.
- AA foundation model is pre-trained on broad data and can be adapted to many downstream tasks rather than just one. Correct
- BA discriminative classifier generates novel samples that resemble its training data instead of labelling inputs.
- CA generative model produces new content such as text or images, while a discriminative model assigns inputs to categories. Correct
- DA foundation model is purpose-built for one narrow task and must be retrained from scratch for any other use.
- EA generative language model selects its output by returning one fixed class label drawn from a closed list.
Why A is correct: Broad pre-training and adaptability across tasks are the defining traits of a foundation model, so this statement is correct.
Why B is wrong: Generating novel samples is a generative behaviour; a discriminative classifier predicts a label and does not synthesise new data.
Why C is correct: Generating fresh content versus drawing a boundary between classes is the core distinction between the two model types.
Why D is wrong: This inverts the concept; the value of a foundation model is reuse across tasks, not single-task lock-in requiring retraining.
Why E is wrong: Returning a label from a fixed list describes classification; a generative model builds open-ended output token by token instead.