AWS Certified AI Practitioner Practice Exam

The term "embeddings" refers to the numerical representations used by AI and Natural Language Processing (NLP) models to understand textual data. In the context of machine learning, embeddings convert words, phrases, or even entire documents into a continuous vector space, facilitating the model's ability to comprehend and manipulate semantic information. This process allows words with similar meanings to have similar representations in a high-dimensional space, effectively capturing context and relationships among words.

Embeddings are crucial for tasks in NLP, such as sentiment analysis, language translation, and text classification, as they enable the models to recognize patterns and make predictions based on the underlying meaning of the text rather than just its surface structure.

Tokens typically refer to the individual units of text, such as words or subwords, that are input into a model, but they do not encapsulate the concept of the numerical representation used for computation. Models represent the structures and algorithms that process data but do not specifically denote the numerical forms of textual data. Binaries pertain to data represented in terms of 0s and 1s, which is a more generic term and does not specifically apply to the realm of textual representation in AI and NLP.