AIF-C01 practice question: Generative AI concepts (1 of 3)
Fundamentals of Generative AI A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
A recruitment site wants a search feature that finds CVs with a similar meaning to a job description, even when the candidate uses different words, such as 'pipeline developer' for 'data engineer'.
The index must cover two million CVs, so simple keyword matching is not enough.
Which concept makes meaning-based matching possible?
Choose one answer.
AA. Embeddings, which turn each text into a vector of numbers where similar meanings sit close together.
BB. Stop sequences, which tell the model where to end the text it generates.
CC. Temperature, which controls how random the model's generated text is.
DD. Tokens, which split each CV into word pieces that the model counts when it processes text for generation.
Show the answer and explanation
Answer: A
A. Right: Correct. An embedding model turns each CV and job description into a vector, and the site compares vectors to find close matches. Different words with a similar meaning produce vectors that sit near each other. Learn more: Amazon Bedrock Knowledge Bases (opens in a new tab)
B. Wrong: A stop sequence marks where generated output should end. It does not compare the meaning of two documents, so it cannot match CVs to a job.
C. Wrong: Temperature controls how varied generated text is. It has no role in comparing CVs with a job description.
D. Wrong: Tokens are pieces of text that models count and process. Splitting text into tokens does not capture meaning, so matching on them still misses different wording.
Explanation
Embeddings map text to numeric vectors where closeness reflects similarity of meaning, which lets a search find CVs that use different words for the same skills. Stop sequences, temperature and tokens control or measure generation and do not compare meaning. This question tests recognizing the concept behind semantic search.
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