AWS Certified Cloud Practitioner
Get started today
Ultimate access to all questions.
Deep dive into the quiz with AI chat providers.
We prepare a focused prompt with your quiz and certificate details so each AI can offer a more tailored, in-depth explanation.
You are analyzing a large collection of news articles and want to group them into topics automatically — without having topic labels. Which method should you use?
Real Exam
Community
RRitesh
A
Supervised Learning
B
Reinforcement Learning
C
Unsupervised Learning (Clustering)
D
Transfer Learning
Explanation:
Explanation
Unsupervised Learning (Clustering) is the correct choice because:
-
No pre-labeled data: The problem states "without having topic labels," which means there are no predefined categories or labels for the news articles
-
Grouping similar items: Clustering algorithms automatically discover patterns and group similar documents together based on their content
-
Topic discovery: This approach identifies natural groupings in the data, which can reveal topics that weren't predefined
Why other options are incorrect:
-
A) Supervised Learning: Requires labeled training data with known topic categories
-
B) Reinforcement Learning: Focuses on decision-making through trial and error with rewards/penalties, not document grouping
-
D) Transfer Learning: Involves applying knowledge from one domain to another, but still typically requires some labeled data
Clustering algorithms like K-means, hierarchical clustering, or topic modeling techniques (LDA) are specifically designed for this type of unsupervised grouping task.
Comments
Loading comments...