AWS Certified Cloud Practitioner

Explanation

AI models count tokens instead of characters because:

• Tokens provide language-independent representation: Tokens represent meaningful units of text (words, subwords, or characters) that work consistently across different languages, unlike character counting which can vary significantly between languages.

• Efficient processing: Tokens allow models to process text more efficiently by breaking it down into meaningful units rather than individual characters.

• Better semantic understanding: Tokenization helps models understand the semantic meaning of text better than character-by-character processing.

• Consistent input size: Token counting provides a more consistent measure of text length across different writing systems and languages.

Why other options are incorrect:

• B: Tokens are not always shorter than characters - in some cases, tokens can represent multiple characters.
• C: Characters can indeed be encoded numerically (using ASCII, Unicode, etc.).
• D: Tokens don't reduce context-window cost to zero - they help manage computational costs more efficiently, but don't eliminate them entirely.