AIF-C01 fundamentals of AI and ML explained
Fundamentals of AI and ML is worth 20% of AIF-C01’s scored content — around 10 of the 50 scored questions. It is the vocabulary layer, and everything in the other four domains is expressed in it. Get this wrong and you will misread questions elsewhere.
The nesting
Learn this as concentric circles, because questions test the distinction:
- Artificial intelligence — the broad field of systems performing tasks that need human-like intelligence.
- Machine learning — a subset where systems learn patterns from data rather than following written rules.
- Deep learning — a subset of ML using multi-layered neural networks, behind most modern vision and language capability.
- Generative AI — applications producing new content, built on deep learning.
The three learning types
| Type | Data | Typical use |
|---|---|---|
| Supervised | Labelled — inputs with known answers | Classification, regression |
| Unsupervised | Unlabelled | Clustering, anomaly detection, grouping |
| Reinforcement | Rewards from an environment | Sequential decisions, robotics, game playing |
The exam’s usual framing: a scenario describes the data available, and you identify the learning type. If the historical data includes known outcomes, it is supervised. If you are looking for structure nobody has labelled, it is unsupervised.
Classification versus regression
Both supervised, and the most reliably tested distinction in the domain:
- Classification predicts a category — spam or not, which of five defect types, will this customer churn.
- Regression predicts a continuous number — next quarter’s revenue, tomorrow’s temperature, expected lifetime value.
If the answer is a number on a scale, it is regression. If it is a label from a set, it is classification.
Training and inference
Two distinct stages:
- Training — the model learns from data. Expensive, occasional.
- Inference — the trained model produces output for new input. Cheaper per call, continuous.
Much of the cost and architecture discussion in later domains rests on this split.
The terms worth knowing cold
- Features — the inputs. Labels — the known answers in supervised learning.
- Overfitting — the model memorised the training data and performs poorly on new data. Underfitting — it never learned the pattern.
- Bias — systematic error, including unfair error between groups. Variance — sensitivity to the particular training sample.
- Model — the learned artefact you deploy.
Matching problems to AWS services
A substantial share of this exam is recognising which service addresses a described need. At foundational level you need purpose, not configuration:
- Amazon SageMaker AI — the platform for building, training and deploying models.
- Amazon Bedrock — managed access to foundation models.
- Pre-trained AI services for vision, speech, language and document processing, which solve common problems without training anything.
The instinct AWS rewards: if a managed service already does it, use the managed service. Building a custom model when a pre-trained service exists is rarely the expected answer at this level.
Sample questions
Question 1. A retailer has transaction data with no labels and wants to discover natural groupings of customers for marketing. Which approach fits?
- A. Supervised classification
- B. Supervised regression
- C. Unsupervised clustering
- D. Reinforcement learning
Show answer
Answer: C
Finding natural groupings in unlabelled data is clustering, an unsupervised technique. Classification and regression are supervised and require labelled outcomes, and reinforcement learning trains an agent through rewards from an environment.
Question 2. A model scores 99% on the data it was trained on but 62% on new data it has never seen. What does this indicate?
- A. Overfitting
- B. Underfitting
- C. Insufficient inference capacity
- D. A context window that is too small
Show answer
Answer: A
Excellent performance on training data with poor performance on unseen data is the definition of overfitting: the model memorised the training set rather than learning a generalisable pattern. Underfitting would show poor performance on both, and neither insufficient inference capacity nor a small context window explains the gap.
Question 3. A company needs to extract text from scanned invoices. It has no machine learning staff and wants the fastest route to a working solution. What should it use?
- A. Train a custom model in Amazon SageMaker AI
- B. A pre-trained AWS AI service for document text extraction
- C. Hire a machine learning team to build a solution
- D. Build a neural network from scratch on Amazon EC2
Show answer
Answer: B
A pre-trained managed AWS service for document text extraction solves a common problem with no model training and no ML expertise, which is what the constraints require. Training a custom model, hiring a team or building a neural network from scratch all add cost and time the scenario rules out.
What to practise
Write one sentence for each term above in your own words, then for each of five business problems name the learning type and the AWS service you would reach for. That exercise is most of this domain, and the service-matching habit carries into every other domain on the exam.