Struggling with unsupervised machine learning concepts for the AI-900 exam? Discover key practice questions like labeling flowers via clustering and master Azure AI Fundamentals scenarios to ace your certification.
Table of Contents
Question
Which of the following is a potential use case for using an unsupervised machine learning model?
A. Labeling flowers based on similarities between their features
B. Predicting house prices based on features such as size and age
C. Classifying items into two categories based on input features
D. Creating a diagnosis for a patient based on specific input metrics
Answer
A. Labeling flowers based on similarities between their features
Explanation
Labeling flowers based on similarities between their features is a potential use case for using the unsupervised machine learning model known as clustering. With clustering, the model analyzes features such as color, shape, and petal count to group similar flowers together, potentially discovering new categories or patterns that were not predefined.
Predicting house prices based on features such as size and age is not a potential use case for using an unsupervised machine learning model. Predicting house prices is a regression task that requires predicting a continuous numerical value (price) based on input features (size, age). This involves learning the relationship between features and labels, which falls under supervised learning where labeled data (house prices) is used for training.
Classifying items into two categories based on input features is not a potential use case for using an unsupervised machine learning model. Classifying data points into predefined categories is typically handled by supervised learning algorithms such as logistic regression or decision trees, which are trained on labeled data.
Creating a diagnosis for a patient based on specific input metrics is not a potential use case for using an unsupervised machine learning model. Creating a diagnosis requires predicting a specific outcome (diagnosis) based on input features (patient metrics). This is a classification task requiring labeled data (existing diagnoses) for training, making it suitable for supervised learning techniques.
Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.