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 learningThree terms used as though they were one thing, and why the difference decides what you can promise about a system.02Supervised, unsupervised and reinforcement learningThe three ways a model learns, what each one needs from your data, and how to tell which problem you are actually holding.03The machine learning lifecycleThe stages between a business question and a model in production, and why the work does not stop at deployment.04Evaluating a machine learning modelWhy accuracy is usually the wrong number, what precision and recall are really trading against each other, 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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