Domain 1 of 5

Fundamentals of AI and ML

The vocabulary, the difference between artificial intelligence, machine learning and deep learning, the three ways a model learns, the stages of a machine learning pipeline and how a model is judged once it exists.

Concepts

~20%

Of the exam

Practice questions

Concepts in this domain

01AI, machine learning and deep learningArtificial intelligence, machine learning and deep learning are three nested categories, and the difference between them decides what a team can promise about a system.02Supervised, unsupervised and reinforcement learningThe three ways a model learns, what each one needs from the data, and how to tell which problem is actually in hand.03The machine learning lifecycleThe stages between a business question and a model in production, why the work does not stop at deployment, and where each cloud vendor's tooling sits along the line.04Evaluating a machine learning modelWhy accuracy is usually the wrong number, what precision and recall are really trading against each other, why the threshold is a product decision and the business metrics that decide whether any of it was worth doing.

Try a question from this domain

A logistics company has a system that decides whether a shipment needs a customs declaration. The logic was written by a compliance team as a set of conditions taken from published trade rules, and it is updated whenever the rules change. A stakeholder asks whether the company is already using machine learning. What is the accurate answer?

AYes. Any system that automates a decision a person used to make is machine learning by definition.
BYes, because the conditions were derived from real trade data held by the compliance team.
CNo. The behaviour was specified by people rather than learned from data, so it is artificial intelligence without machine learning.
DNo, and it is not artificial intelligence either, because no model is involved.

13 questions on this domain.

One per page, with a worked explanation.

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