AWS Certified AI Practitioner · Free Practice Question Medium
Question 23
A healthcare company is evaluating the use of Foundation Models (FMs) in generative AI to automate tasks such as medical report generation, data analysis, and personalized patient communications. The company's data science team wants to better understand the key features and benefits of Foundation Models, particularly how they can be applied to various tasks with minimal fine-tuning and customization. To ensure they choose the right model for their needs, the team is seeking to clarify the essential characteristics of FMs in generative AI.
Which of the following is correct regarding Foundation Models (FMs) in the context of generative AI?
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A
FMs use labeled training data sets for supervised learning
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B
FMs use unlabeled training data sets for self-supervised learning
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C
FMs use unlabeled training data sets for supervised learning
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D
FMs use labeled training data sets for self-supervised learning
Reveal correct answer
Correct answer: B
Explanation
Correct option:
FMs use unlabeled training data sets for self-supervised learning
In supervised learning, you train the model with a set of input data and a corresponding set of paired labeled output data. Unsupervised machine learning is when you give the algorithm input data without any labeled output data. Then, on its own, the algorithm identifies patterns and relationships in and between the data. Self-supervised learning is a machine learning approach that applies unsupervised learning methods to tasks usually requiring supervised learning. Instead of using labeled datasets for guidance, self-supervised models create implicit labels from unstructured data.
Foundation models use self-supervised learning to create labels from input data. This means no one has instructed or trained the model with labeled training data sets.
Incorrect options:
FMs use labeled training data sets for self-supervised learning
FMs use labeled training data sets for supervised learning
FMs use unlabeled training data sets for supervised learning
These three options contradict the explanation provided above, so these options are incorrect.
References:
https://aws.amazon.com/what-is/foundation-models/
https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models-fine-tuning.html
https://aws.amazon.com/compare/the-difference-between-machine-learning-supervised-and-unsupervised/
Discussion
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