Paper 1 AI and ML Drills
These are original topic-local Paper 1-style drills, not a complete 100-mark Paper 1. For this 2027 module, question style is inferred from general Computing assessment patterns rather than from past AI/ML papers.
Detailed answers are in Paper 1 AI and ML Answers.
Revise the topic hub first:
Questions
Question 1: AI Definition
Define Artificial Intelligence in the context of computing. [2]
Question 2: AI Task Examples
Give four examples of tasks that can be performed well by AI systems. [4]
Question 3: ML Versus Traditional Programming
A programmer wants to classify emails as spam or not spam.
Explain one difference between solving this task using traditional programming and solving it using machine learning. [3]
Question 4: Supervised or Unsupervised
For each scenario, state whether supervised or unsupervised learning is more suitable. Give a reason.
| Scenario | Learning type and reason |
|---|---|
A set of past images is labelled as cat or dog; a model should classify new images. | |
| A shop has customer spending records but no customer group labels; it wants to find groups of similar customers. |
[4]
Question 5: k-NN
A k-NN classifier stores labelled examples.
Explain how it predicts the label of a new data item. [4]
Question 6: k-Means
State the purpose of k-means and describe the two repeated steps used after initial centres have been chosen. [4]
Question 7: Meaning of k
Explain the meaning of in k-nearest neighbours and in k-means. [2]
Question 8: ML Workflow
Arrange these ML workflow steps in the standard ML workflow order used in this topic:
evaluate model
prepare data
make predictions
gather data
choose model
train model
tune parameters[3]
Question 9: Problem Formulation
A school wants to predict whether a student is likely to submit homework late. It has records containing number of previous late submissions, attendance percentage, and whether the next homework was late.
Identify:
- two possible input features;
- the label;
- whether this is supervised or unsupervised learning.
[4]
Question 10: Model Evaluation
An ML model makes these predictions:
| Item | Predicted | Actual |
|---|---|---|
| 1 | spam | spam |
| 2 | not spam | spam |
| 3 | spam | spam |
| 4 | not spam | not spam |
Calculate the accuracy. [2]
Review Checklist
After attempting these questions, check whether you can:
- define AI and ML accurately;
- give concrete AI task examples;
- distinguish traditional programming from ML;
- identify supervised and unsupervised learning scenarios;
- explain k-NN and k-means without confusing their use of ;
- order the ML workflow;
- calculate simple accuracy.