A company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company's products. Which…

The correct answer is B. Unsupervised learning. Unsupervised learning is the correct methodology for classifying customers into tiers when the data is unlabeled, as it does not require predefined labels or outputs. Unsupervised Learning: This type of machine learning is used when the data has no labels or pre-defined…

Submitted by akirajp· Mar 30, 2026Machine Learning Concepts

Question

A company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company's products. Which methodology should the company use to meet these requirements?

Options

ASupervised learning
BUnsupervised learning
CReinforcement learning
DReinforcement learning from human feedback (RLHF)

How the community answered

(32 responses)

A
6% (2)
B
72% (23)
C
6% (2)
D
16% (5)

Explanation

Unsupervised learning is the correct methodology for classifying customers into tiers when the data is unlabeled, as it does not require predefined labels or outputs. Unsupervised Learning: This type of machine learning is used when the data has no labels or pre-defined categories. The goal is to identify patterns, clusters, or associations within the data. In this case, the company has petabytes of unlabeled customer data and needs to classify customers into different tiers. Unsupervised learning techniques like clustering (e.g., K-Means, Hierarchical Clustering) can group similar customers based on various attributes without any prior knowledge or labels.

Topics

#Unsupervised learning#Customer segmentation#Unlabeled data#Machine learning methodology

Community Discussion

No community discussion yet for this question.

Full AIF-C01 Practice