AIF-C01 - Fundamentals of AI and ML (20% of the exam) - Section 1.1
Explain basic AI concepts and terminologies.
Define artificial intelligence, machine learning, and deep learning, and place them in relation to one another. Recognise common terms such as inference, training, model, and dataset, and the difference between supervised, unsupervised, and reinforcement learning.
Machine learningDeep learningInference
Practice question for this objective
lock_openFree sampleFundamentals of AI and MLeasy
A new analyst is learning how the terms artificial intelligence, machine learning, and deep learning relate to one another as nested fields. Which two statements correctly describe this relationship? Select TWO.
AArtificial intelligence is one specialised technique that sits entirely inside the broader field of machine learning.
BMachine learning is a subset of artificial intelligence in which systems learn patterns from data rather than following hand-coded rules.check_circle Correct
CDeep learning and machine learning are two separate disciplines that share no methods and never overlap in practice.
DDeep learning is a subset of machine learning that uses neural networks with many stacked layers to learn features.check_circle Correct
EArtificial intelligence, machine learning, and deep learning are interchangeable synonyms for exactly the same single concept.
Deep learning sits inside machine learning, which in turn sits inside artificial intelligence, forming three nested fields of progressively narrower scope. The three terms form concentric sets: artificial intelligence is the broadest goal of machine intelligence, machine learning is the subset that learns from data, and deep learning is the further subset using many-layered neural networks. Correctly ordering this containment is the core terminology distinction.
Why A is wrong: This reverses the actual nesting; machine learning is a subset of artificial intelligence, not the other way around, so the containment is backwards.
Why B is correct: Machine learning is correctly framed as a branch of the wider artificial intelligence field, defined by learning from data instead of explicit rules.
Why C is wrong: This is tempting because the names differ, but deep learning is a subset of machine learning, so the two clearly overlap rather than being separate.
Why D is correct: Deep learning is accurately placed inside machine learning and characterised by multi-layer neural networks that learn representations directly.
Why E is wrong: Treating the three as identical ignores their nesting; each names a progressively narrower field, so they are related but not interchangeable.
Answers that look right on this material and are not. Each one is a distractor from a different question in the AIF-C01 bank for this domain.
Grouping customers into an unknown number of segments using only their purchase histories and no target values.
errorWhy it is wrong: This is tempting as a learning task, but with no target labels and unknown groups it is unsupervised clustering, not supervised learning.
Deep learning and machine learning are independent disciplines that both contribute to the broader field of artificial intelligence.
errorWhy it is wrong: Tempting because all three terms appear in the same conversations, but machine learning and deep learning are not independent sibling fields. Deep learning is a subset of machine learning, not a parallel branch.
Training is the phase where the model produces predictions for users, while inference is the phase where engineers gather and clean the example data beforehand.
errorWhy it is wrong: This reverses the two terms and confuses inference with data preparation; producing predictions for users is inference, and gathering data is a separate preparation step, so the mapping is wrong.