Databricks Certified Professional Data Scientist Exam - Topic 3 Question 56 Discussion

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

Question #: 56
Topic #: 3

[All Databricks Certified Professional Data Scientist Questions]

Which is an example of supervised learning?

APCA

Bk-means clustering

CSVD

DEM

ESVM
SVMs can be used to solve various real world problems:
* SVMs are helpful in text and hypertext categorization as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.
* Classification of images can also be performed using SVMs. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
* SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly.
* Hand-written characters can be recognized using SVM

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Suggested Answer: B

by Kerry at Apr 13, 2024, 02:36 AM

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Raylene

4 months ago

SVMs can classify images? That's impressive!

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Cristina

5 months ago

K-means clustering is not supervised, just saying!

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Becky

5 months ago

Wait, PCA is unsupervised, right?

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Yvonne

5 months ago

Totally agree, SVMs are super effective for classification.

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Kiley

5 months ago

SVM is definitely supervised learning!

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Teresita

6 months ago

I’m a bit confused; I thought k-means clustering was also a type of supervised learning, but now I’m not so sure.

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Bettyann

6 months ago

I practiced a similar question, and I believe SVM is the right choice here because it’s explicitly designed for supervised tasks.

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Jaime

6 months ago

I’m not entirely sure, but I remember PCA and SVD are more about dimensionality reduction, which doesn’t fit supervised learning.

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Nichelle

6 months ago

I think supervised learning involves labeled data, so I’m leaning towards SVM since it’s used for classification tasks.

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Laquanda

6 months ago

I've got this! Supervised learning is all about using labeled data to train a model, and SVM (option E) is a classic example of a supervised learning algorithm. I'm feeling good about this one.

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Ashley

6 months ago

I'm a little confused by this question. I know supervised learning is when the algorithm is trained on labeled data, but I'm not sure which of these options fits that description best.

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Darrel

6 months ago

Okay, let me think this through. Supervised learning is when the algorithm is trained on labeled data to make predictions. I think option E, SVM, is the best example here.

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Susy

6 months ago

Hmm, I'm a bit unsure about this one. I know supervised learning involves labeled data, but I'm not sure which of these options is the best example.

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Bulah

6 months ago

This looks like a straightforward question on supervised learning. I'm pretty confident I can identify the correct answer.

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Oneida

6 months ago

I'm pretty confident I know the answer to this one. A flow port that is typed by a flow specification is considered nonatomic, since it's a composite structure made up of multiple parts.

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Ty

6 months ago

I'm pretty confident that C is the correct answer here. It covers the key components of a production plan that I'm familiar with from our course material.

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Amber

6 months ago

I think I know the answer to this one. The connection between virtual servers is a secondary physical connection, and it's established using the Redundant Physical Connection for Virtual Servers pattern.

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Cyril

6 months ago

Okay, this looks pretty straightforward. I think I can handle this.

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Clarence

11 months ago

SVM? More like 'Super Versatile Machine'! It can do all sorts of cool stuff, like classifying proteins and recognizing handwritten characters. Who needs a human when you've got an SVM, am I right?

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Mike

10 months ago

It's amazing how SVMs can be applied to so many different fields, from text categorization to medical science.

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Amie

10 months ago

Yeah, SVMs are like the Swiss Army knife of machine learning algorithms.

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Jamey

10 months ago

I agree, SVMs are really versatile and can be used for a wide range of tasks.

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Laurene

11 months ago

SVM stands for Support Vector Machines, and it's a powerful supervised learning algorithm that can handle complex, high-dimensional data. It's a great choice for this question.

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Billy

10 months ago

SVMs are really versatile and can be used in many different applications

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Ilene

11 months ago

Yes, SVM is an example of supervised learning

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Wilburn

11 months ago

I think the answer is E) SVM

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Quentin

12 months ago

PCA and k-means are both unsupervised learning techniques, so they wouldn't be examples of supervised learning. SVD and EM are also not supervised learning.

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Erinn

10 months ago

Classification of images can also be performed using SVMs. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.

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