AWS Certified AI Practitioner AIF-C01 Practice Question

Embeddings transform text, images, or other data into dense numerical vectors that capture semantic meaning. By embedding both user preferences and item metadata in the same vector space, the recommendation engine can calculate vector distances to surface the most similar items for each user. Tokenization is only a preprocessing step that splits input into smaller units, prompt engineering shapes model input rather than representing similarity, and diffusion models are primarily used for generating images or other media-not for matching items in a vector space.