Databricks Certified Professional Data Scientist Exam - 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.

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Suggested Answer: B

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

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Candra

5 months ago

I’m not sure about that, seems like it could be more complex.

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Rose

5 months ago

Definitely, option B is spot on!

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Maira

6 months ago

Wait, so it doesn't aim to maximize anything? That's surprising!

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Elden

6 months ago

Totally agree, it's not about maximizing utility!

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Alise

6 months ago

Clustering is all about finding similarities in data.

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Thad

6 months ago

I feel like I read that clustering doesn't aim to maximize a utility function, so maybe E is the right choice? But I'm not entirely confident.

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Lorenza

6 months ago

I practiced a question similar to this, and I think the answer might be D, since it combines both finding similarities and not maximizing a utility function.

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Dyan

6 months ago

I think option B is definitely correct since clustering focuses on similarities, but I’m a bit confused about option C.

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Justa

6 months ago

I remember clustering is about finding similarities in data, but I'm not sure if maximizing a utility function is relevant here.

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Alaine

6 months ago

Hmm, I'm not entirely sure about this one. I know there are a few different Handlebars helper functions related to pricing, but I can't remember which one is specifically used for formatting. I'll have to think this through carefully.

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Francesco

6 months ago

Acceptance of the audit risks and opportunities is an interesting choice, but I'm not sure that would be the most important thing to provide upfront. I think I'll go with the scope and stakeholders as the best answer here.

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Stephane

6 months ago

Okay, let's think this through step-by-step. The key is ensuring App1 supports the new multi-region write configuration for account1 while meeting the business and product catalog requirements.

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Ranee

11 months ago

This question really makes me appreciate the importance of understanding the fundamentals of unsupervised learning. Clustering is a powerful technique, but you have to know what it's actually doing.

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Lottie

10 months ago

D) 1 and 2

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Rosamond

10 months ago

The question really highlights the importance of understanding unsupervised learning.

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An

10 months ago

B) Find similarities in the training data

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Cherilyn

10 months ago

The goal is not to maximize a utility function, but simply to find similarities in the training data.

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Sharen

10 months ago

B) Find similarities in the training data

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Cherry

10 months ago

A) Maximize a utility function

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Helaine

10 months ago

A) Maximize a utility function

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Quinn

11 months ago

Haha, I bet the person who wrote this question was trying to trip us up with that 'not to maximize a utility function' option. Nice try, but I'm on to you!

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Eve

10 months ago

E) 2 and 3

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Alverta

10 months ago

B) Find similarities in the training data

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Lavelle

10 months ago

A) Maximize a utility function

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Merri

11 months ago

Hmm, I was a bit unsure about the difference between maximizing a utility function and just finding similarities. Glad the explanation clarified that for me.

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