AWS Certified Machine Learning Engineer Associate · Free Practice Question Medium
Question 11
Which of the following summarizes the differences between a token and an embedding in the context of generative AI?
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A
An embedding is a sequence of characters that a model can interpret or predict as a single unit of meaning, whereas, a token is a vector of numerical values that represents condensed information obtained by transforming input into that vector
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B
Both token and embedding refer to a sequence of characters that a model can interpret or predict as a single unit of meaning
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C
Both token and embedding refer to a vector of numerical values that represents condensed information obtained by transforming input into that vector
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D
A token is a sequence of characters that a model can interpret or predict as a single unit of meaning, whereas, an embedding is a vector of numerical values that represents condensed information obtained by transforming input into that vector
Reveal correct answer
Correct answer: D
Explanation
Correct option:
A token is a sequence of characters that a model can interpret or predict as a single unit of meaning, whereas, an embedding is a vector of numerical values that represents condensed information obtained by transforming input into that vector
Embedding – The process of condensing information by transforming input into a vector of numerical values, known as the embeddings, in order to compare the similarity between different objects by using a shared numerical representation. For example, sentences can be compared to determine the similarity in meaning, images can be compared to determine visual similarity, or text and image can be compared to see if they're relevant to each other.
Token – A sequence of characters that a model can interpret or predict as a single unit of meaning. For example, with text models, a token could correspond not just to a word, but also to a part of a word with grammatical meaning (such as "-ed"), a punctuation mark (such as "?"), or a common phrase (such as "a lot").
Incorrect options:
Both token and embedding refer to a sequence of characters that a model can interpret or predict as a single unit of meaning
An embedding is a sequence of characters that a model can interpret or predict as a single unit of meaning, whereas, a token is a vector of numerical values that represents condensed information obtained by transforming input into that vector
Both token and embedding refer to a vector of numerical values that represents condensed information obtained by transforming input into that vector
These three options contradict the explanation provided above, so these options are incorrect.
References:
Discussion
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