AWS Certified AI Practitioner (AIF-C01) — Question 184

What is the purpose of vector embeddings in a large language model (LLM)?

Answer options

• A. Splitting text into manageable pieces of data
• B. Grouping a set of characters to be treated as a single unit
• C. Providing the ability to mathematically compare texts
• D. Providing the count of every word in the input

Correct answer: C

Explanation

The correct answer, C, is right because vector embeddings allow for mathematical comparisons of texts by representing them in a continuous vector space. Options A and B describe text preprocessing techniques, while option D focuses solely on word frequency, which does not capture the relational semantics that embeddings provide.