Unsupervised Learning CT-AI Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   Which of the following is a common clustering algorithm?

   • ALinear regression
   • BLogistic regression
   • CK-means
   • DDecision tree
   Reveal Solution Discussion Select an answer first
2. 7application · medium

   A machine learning engineer is deciding between supervised and unsupervised learning for a project. The dataset has no output labels, and the goal is to discover hidden patterns. Which statement correctly describes the appropriate approach?

   • AUse supervised learning because it can infer labels from the data.
   • BUse unsupervised learning because it works without labels and finds hidden structure.
   • CUse supervised learning with a regression algorithm to predict a continuous output.
   • DUse unsupervised learning with a classification algorithm to assign labels.
   Reveal Solution Discussion Select an answer first
4. 8foundation · easy

   Which of the following is a typical application of association rule learning?

   • AReducing the number of features for a machine learning model.
   • BMarket basket analysis to find products frequently purchased together.
   • CPredicting house prices based on features.
   • DImage classification to identify objects in photos.
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5. 9application · medium

   A retail company has a dataset of customer purchase histories with no predefined customer categories. They want to identify natural groupings of customers based on purchasing behavior to tailor marketing campaigns. Which approach should they use?

   • ATrain a supervised classifier using historical purchase data labeled with customer segments.
   • BApply k-means clustering to the purchase data to discover customer segments.
   • CUse linear regression to predict future purchase amounts for each customer.
   • DPerform association rule mining to find items frequently bought together.
   Reveal Solution Discussion Select an answer first
6. 10foundation · easy

   Which statement correctly describes the key difference between supervised and unsupervised learning?

   • ASupervised learning uses labeled data to train a model, while unsupervised learning finds patterns in unlabeled data.
   • BSupervised learning requires a large dataset, while unsupervised learning works only with small datasets.
   • CSupervised learning does not require data cleaning, while unsupervised learning does.
   • DSupervised learning is used for prediction, while unsupervised learning is used only for data visualization.
   Reveal Solution Discussion Select an answer first