Databricks Exam Databricks-Certified-Professional-Data-Scientist Topic 2 Question 39 Discussion

Actual exam question for Databricks's Databricks-Certified-Professional-Data-Scientist exam

Question #: 39
Topic #: 2

[All Databricks-Certified-Professional-Data-Scientist Questions]

Clustering is a type of unsupervised learning with the following goals

AMaximize a utility function

BFind similarities in the training data

CNot to maximize a utility function

D1 and 2

E2 and 3
type of unsupervised learning is called clustering. In this type of learning, The goal is not to maximize a utility function, but simply to find similarities in the training data.
The assumption is often that the clusters discovered will match reasonably well with an intuitive classification. For instance, clustering individuals based on demographics might result in a clustering of the wealthy in one group and the poor in another. Clustering can be useful when there is enough data to form clusters (though this turns out to be difficult at times) and especially when additional data about members of a cluster can be used to produce further results due to dependencies in the data.

Show Suggested Answer

Suggested Answer: B

by Jovita at Mar 22, 2023, 09:45 AM

Limited Time Offer

25%

Off

Get Premium Databricks-Certified-Professional-Data-Scientist Questions as Interactive Web-Based Practice Test or PDF