AWS Certified Cloud Practitioner
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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
0
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.
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