Quiz – Foundation Models Explained

Question 1 of 5

1. Question

How are foundation models trained, unlike traditional single-task systems?

They are pre-trained on massive, diverse datasets using self-supervised learning

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They are hand-coded with explicit rules for one task

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They are trained only on a few labeled examples

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They require a fresh model built from scratch for each problem

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Question 2 of 5

2. Question

What enables foundation models to adapt to new tasks with minimal effort?

Transfer learning with minimal fine-tuning

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Deleting the pre-training data before each task

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Manually rewriting the model for every new domain

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Running the model without any training at all

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Question 3 of 5

3. Question

Who demonstrated the pre-training phase with BERT, according to the capsule?

Devlin

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Goodfellow

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Mikolov

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Turing

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Question 4 of 5

4. Question

On which architecture are foundation models built?

Transformer neural networks

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Rule-based decision trees

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Relational databases

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Simple linear regression models

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Question 5 of 5

5. Question

How are foundation models applied in finance?

They analyze market sentiment and risk

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They power diagnostic support systems

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They enable personalized learning pathways

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They generate marketing images for campaigns

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