Databricks Certified Professional Data Scientist Exam - Topic 4 Question 78 Discussion

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

Question #: 78
Topic #: 4

[All Databricks Certified Professional Data Scientist Questions]

Select the correct option from the below

AIf you're trying to predict or forecast a target value^ then you need to look into supervised learning.

BIf you've chosen supervised learning, with discrete target value like Yes/No. 1/2/3, A/B/C: or Red/Yellow/Black, then look into classification.

CIf the target value can take on a number of values, say any value from 0.00 to 100.00, or -999 to 999: or +_to -_, then you need to look unsupervised learning

DIf you're not trying to predict a target value, then you need to look into unsupervised learning

EAre you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering.
If you re trying to predict or forecast a target value, then you need to look into supervised learning. If not, then unsupervised learning is the place you want to be. If you've chosen supervised learning, what's your target value? Is it a discrete value like Yes/No, 1/2/3, A/B/C: or Red/Yellow/Black? If so, then you want to look into classification. If the target value can take on a number of values, say any value from 0.00 to 100.00, or-999 to 999, or+_to -_, then you need to look into regression. If you're not trying to predict a target value: then you need to look into unsupervised learning. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering. Do you need to have some numerical estimate of how strong the fit is into each group? If you answer yes then you probably should look into a density estimation algorithm.

Show Suggested Answer

Suggested Answer: C

by Terrilyn at Jan 28, 2025, 10:43 AM

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Alishia

4 months ago

Clustering is just grouping, not predicting.

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Helaine

5 months ago

Regression is for continuous target values, right?

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Jeannetta

5 months ago

Wait, unsupervised learning for non-predictive tasks? Really?

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Jaime

5 months ago

Totally agree, classification is for discrete values.

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Goldie

5 months ago

Supervised learning is key for predictions!

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Dalene

5 months ago

I vaguely recall that if the target can be any number, then we should look at regression, but I'm not entirely confident about the details.

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Willetta

6 months ago

I feel a bit confused about unsupervised learning. I thought it was only for when we don't have a target value, but E seems to suggest clustering is also part of it.

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Giovanna

6 months ago

I remember practicing a question where we had to distinguish between regression and classification in supervised learning. I think B is correct for discrete targets.

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Kelvin

6 months ago

I think supervised learning is definitely about predicting target values, but I'm not sure if classification is the right term for discrete values.

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Maricela

6 months ago

I'm a bit confused by all the different options here. I'll need to re-read the question carefully and make sure I understand the key distinctions between supervised and unsupervised learning, as well as classification and regression. Taking some notes might help me work through this step-by-step.

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Sena

6 months ago

Okay, I think I've got this. If I'm trying to predict a target value, supervised learning is the way to go. Then I need to figure out if it's a discrete or continuous target, and choose classification or regression accordingly. Seems pretty straightforward!

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Angella

6 months ago

Hmm, this is a tricky one. I need to carefully consider the nature of my target variable - is it discrete or continuous? That will determine whether I should use classification or regression within the supervised learning framework.

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Laurene

6 months ago

This question seems straightforward. I'll start by identifying whether I'm trying to predict a target value or not. If so, I'll look into supervised learning. If not, unsupervised learning is the way to go.

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Fatima

11 months ago

Supervised, unsupervised, classification, regression, clustering... Sounds like a machine learning alphabet soup! *chuckles* I bet the instructor is having a field day with this one.

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Kenda

10 months ago

Definitely! It's like solving a puzzle with data.

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Brandee

10 months ago

I agree, but once you get the hang of it, it's actually quite fascinating.

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Sharika

10 months ago

Haha, I know right! It can get pretty confusing with all these terms.

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Fausto

11 months ago

Ah, I see. So it all comes down to whether I have a target value to predict or not. This is a great way to break down the problem. *scratches head* Now, what was the question again?

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Irma

10 months ago

Do you need to have some numerical estimate of how strong the fit is into each group? If you answer yes then you probably should look into a density estimation algorithm.

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Simona

10 months ago

If you've chosen supervised learning, what's your target value? Is it a discrete value like Yes/No, 1/2/3, A/B/C: or Red/Yellow/Black? If so, then you want to look into classification.

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Tiera

11 months ago

If you're trying to predict or forecast a target value, then you need to look into supervised learning.

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Francine

12 months ago

Hmm, unsupervised learning if I'm not trying to predict a target. And clustering if I just want to group my data. This exam question is really comprehensive!

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Odette

11 months ago

Yes, this exam question covers all the bases when it comes to machine learning options.

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Merri

11 months ago

Clustering is perfect for grouping your data if that's all you need.

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Hermila

11 months ago

Unsupervised learning is the way to go if you're not predicting a target value.

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Nana

12 months ago

I am not trying to predict a target value, so I think I should go with option D for unsupervised learning.

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Gerald

1 year ago

Supervised learning for predicting a target value? Got it! And classification for discrete targets, regression for continuous ones. This is making sense now.

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