AWS Certified AI Practitioner · Free Practice Question Hard
Question 38
A research-focused AI company is developing a suite of machine learning models for tasks such as classification and content generation. The data science team needs to choose between discriminative and generative models depending on the specific use case. To make the right decision, they need to understand the fundamental differences between these two types of models, particularly in the context of generative AI, and how each model type fits into their project goals.
What is the primary distinction between discriminative models and generative models in the context of generative AI?
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A
Discriminative models are only used for text classification, while generative models are only used for image classification
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B
Generative models are trained on labeled data, while discriminative models can be trained on both labeled and unlabeled data
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C
Generative models focus on generating new data from learned patterns, whereas discriminative models classify data by distinguishing between different classes
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D
Discriminative models are used to generate new data, while generative models are used only for classification
Reveal correct answer
Correct answer: C
Explanation
Correct option:
Generative models focus on generating new data from learned patterns, whereas discriminative models classify data by distinguishing between different classes
Generative models learn the underlying patterns of data to create new, similar data, while discriminative models learn to distinguish between different classes of data. Generative models, such as GPT-3, can generate new content, whereas discriminative models are used for classification tasks. The former focuses on understanding and replicating the data distribution, while the latter focuses on decision boundaries to classify inputs.
For example, discriminative models look at images - known data like pixel arrangement, line, color, and shape — and then map them to an outcome — the unknown factor. Mathematically, these models work by identifying equations that could numerically map unknown and known factors as x and y variables.
Generative models take this one step further. Instead of predicting a label given some features, they try to predict features given a certain label. Mathematically, generative modeling calculates the probability of x and y occurring together. It learns the distribution of different data features and their relationships. For example, generative models analyze animal images to record variables like different ear shapes, eye shapes, tail features, and skin patterns. They learn features and their relations to understand what different animals look like in general. They can then recreate new animal images that were not in the training set.
Incorrect options:
Discriminative models are used to generate new data, while generative models are used only for classification - Discriminative models are used primarily for classification, not for generating new data.
Discriminative models are only used for text classification, while generative models are only used for image classification - Discriminative models can be used for both text and image classification, while generative models learn the underlying patterns of data to create new data.
Generative models are trained on labeled data, while discriminative models can be trained on both labeled and unlabeled data - The training data type (labeled vs. unlabeled) is not the primary distinction between generative and discriminative models.
Reference:
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