A team is building a model to predict customer churn (will leave or stay). They have historical data with a 'churned' label. Which statement correctly describes the learning paradigm?

Supervised learning because the model learns from labeled examples

Semi-supervised learning because some customers have labels

Reinforcement learning because the model learns from customer feedback

Unsupervised learning because the model finds patterns in customer behavior

A company has a dataset of customer transactions. They want to build a model to predict whether a transaction is fraudulent. The dataset includes a 'fraud' label for each transaction, but the label is noisy (some transactions are mislabeled). Which approach is most appropriate?

Use supervised learning without any label cleaning

Use unsupervised learning to avoid the noisy labels

Use reinforcement learning to learn from transaction outcomes

Use supervised learning, but first clean the labels to reduce noise

Which of the following is an example of labeled data for a supervised learning task?

A set of emails, each marked as 'spam' or 'not spam'.

A list of customer ages and incomes without any other information.

A database of sensor readings with timestamps but no recorded events.

A collection of news articles with no topic tags.

A team is building a model to predict the time a customer will spend on a website. The target is a continuous value in minutes. They are considering using a decision tree. What is the main consideration?

Use logistic regression instead of a decision tree

Use a classification tree because the target is continuous

Use a support vector machine for classification

Use a regression tree, not a classification tree

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Domain 3Objective 1

Supervised Learning CT-AI Practice Questions (Page 1)

Part of the Domain 3: Machine Learning (ML) - Overview domain, which makes up ~15% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~4–6 in this domain), expect 1–1 from this objective — we provide 27 practice questions to prepare you well beyond it. (estimate)

27questions here

6free pages

6concepts

Questions 1–5

1application · medium
A team is building a model to predict customer churn (will leave or stay). They have historical data with a 'churned' label. Which statement correctly describes the learning paradigm?
Select an answer first
2foundation · easy
Which algorithm is commonly used for binary classification tasks, such as predicting whether an email is spam or not?
Select an answer first
3expert · hard
A company has a dataset of customer transactions. They want to build a model to predict whether a transaction is fraudulent. The dataset includes a 'fraud' label for each transaction, but the label is noisy (some transactions are mislabeled). Which approach is most appropriate?
Select an answer first
4foundation · easy
Which of the following is an example of labeled data for a supervised learning task?
Select an answer first
5expert · hard
A team is building a model to predict the time a customer will spend on a website. The target is a continuous value in minutes. They are considering using a decision tree. What is the main consideration?
Select an answer first

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