AWS Certified AI Practitioner AIF-C01 Practice Question
When preparing text data for a foundation language model hosted in Amazon Bedrock, which description best captures the tokenization step applied before the text is sent to the model?
Encrypting the text so it remains confidential while moving between the application and Amazon Bedrock.
Splitting each input sentence into sub-word pieces and assigning a numeric ID to every piece the model understands.
Automatically summarizing each text sample to shorten prompts and reduce cost.
Compressing the model's vocabulary so the deployed model occupies less storage space.
Tokenization converts raw text into smaller units-often words, sub-words, or characters-and maps each unit to a numerical identifier. Models process sequences of these numeric tokens, not raw strings. Encryption protects data in transit, but it does not change the representation the model consumes. Compressing the vocabulary or summarizing text are separate optimizations that may occur later, but neither describes the mandatory preprocessing step that enables the model to understand and generate language.
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AWS Certified AI Practitioner AIF-C01
Fundamentals of Generative AI
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