Differentiate the Use Between Different Algorithm Types (supervised, Unsupervised, Reinforcement) CERTIFIED-AI-EXPERT Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   How does supervised learning differ from unsupervised learning in terms of data requirements?

   • ASupervised learning requires labeled data, while unsupervised learning uses unlabeled data.
   • BSupervised learning requires unlabeled data, while unsupervised learning uses labeled data.
   • CBoth require unlabeled data, but unsupervised learning uses more samples.
   • DBoth require labeled data, but supervised learning uses more features.
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2. 17application · medium

   A company has a dataset of customer reviews for its products. They want to automatically categorize each review as positive, negative, or neutral. They have a large set of reviews that have already been manually labeled by their support team. Which algorithm type should they use?

   • ADimensionality reduction, to reduce the number of words in the reviews before analysis.
   • BSupervised learning, because the labeled reviews provide the ground truth for training a classification model.
   • CUnsupervised learning, using clustering to group similar reviews together.
   • DReinforcement learning, by having an agent classify reviews and receive feedback on its accuracy.
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4. 18foundation · easy

   Which task is a common use case for supervised learning?

   • AGrouping customers into segments based on purchasing behavior without predefined categories.
   • BReducing the number of features in a dataset while preserving variance.
   • CTraining an agent to play a video game by maximizing its score.
   • DPredicting whether an email is spam or not based on labeled examples.
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5. 19expert · hard

   A data science team is building a system to detect fraudulent credit card transactions. They have a large dataset of past transactions, but only a small fraction are labeled as fraudulent. The team needs to identify new types of fraud that have not been seen before. Which approach should they use?

   • ASupervised learning only, because the labeled data provides the ground truth for training a classifier.
   • BUnsupervised learning only, because it can find anomalies without needing labels.
   • CReinforcement learning, by having an agent explore transactions and receive rewards for detecting fraud.
   • DA combination of supervised learning for known fraud patterns and unsupervised learning for anomaly detection to catch new fraud types.
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6. 20application · medium

   A healthcare analytics team has a historical dataset of patient records where each record includes a confirmed diagnosis label assigned by physicians. They need to build a model that predicts the diagnosis for new patients based on their symptoms and test results. Which algorithm type should the team use?

   • AReinforcement learning, because the model should learn by interacting with patients and receiving feedback on its predictions.
   • BSupervised learning, because the training data includes labeled outcomes that the model can learn to predict.
   • CSemi-supervised learning, because the model should use a small amount of labeled data and a large amount of unlabeled data.
   • DUnsupervised learning, because the model should discover natural groupings of patients without predefined categories.
   Reveal Solution Discussion Select an answer first