Free cloud certification practice questions

AI, ML, and deep learning form a nested hierarchy from broadest to most specialized.

AI is any technique that makes a machine mimic human intelligence, ML is the subset that learns patterns from data, and deep learning is the subset of ML built on multi-layer neural networks, so option A orders them correctly. The other options reverse or break this nesting, which the exam treats as a clear error.

1. Start with the widest term: AI covers all machine intelligence, including rule-based systems.
2. Place ML inside AI as the data-learning subset, and deep learning inside ML as the neural-network subset.
3. Confirm the order runs AI, then ML, then deep learning from outside to inside.

Exam tip: AI contains ML, and ML contains deep learning.

A token is the small unit of text, often a word or part of a word, that a language model reads and produces.

Language models do not process letters or whole documents directly; they process sequences of tokens, so option A is correct. An embedding is a numerical representation of meaning, an access token is a security credential, and splitting a document is chunking, which is why the other options are wrong.

1. Recall that models work on sequences of small text units.
2. Match the definition of that unit to the term token.
3. Rule out embedding, credential, and chunking as different concepts.

Exam tip: A token is the small unit of text a model reads and generates.