Free cloud certification practice questions

AI, ML, and deep learning nest from broadest to narrowest, and AI also includes systems that do not learn.

AI covers any technique that lets a machine mimic human intelligence, including hand-written rule engines. Machine learning is the subset that learns patterns from data, and deep learning is the subset of ML that uses multi-layer neural networks. A tool that only applies fixed rules is therefore AI but not ML. Calling it neither AI nor ML is the tempting mistake: it treats AI as if it meant only learning systems.

1. Ask whether the system learns from data or only follows rules it was given.
2. Recall that AI is the widest circle and includes rule-based systems.
3. Place ML inside AI and deep learning inside ML, so a non-learning system can be AI without being ML.

Exam tip: AI contains ML, ML contains deep learning, and rule-based systems are AI without being ML.

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

Models do not read whole documents or single letters; they read and produce sequences of tokens, and limits such as the context window are counted in tokens. A list of numbers representing meaning is an embedding, a signed credential is an API access token (a security term that only shares the name), and a section split from a long document is a chunk.

1. Notice that the request limit is expressed in tokens, so a token is a unit of text the model processes.
2. Recall that a token is roughly a word or word piece, about four characters of English.
3. Separate it from embeddings (meaning as numbers), chunks (document sections) and API credentials.

Exam tip: A token is the small unit of text a model reads and generates; context windows and bills are counted in tokens.