Databricks Exam Databricks Certified Professional Data Scientist Topic 5 Question 85 Discussion

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

Question #: 85
Topic #: 5

[All Databricks Certified Professional Data Scientist Questions]

Select the correct objectives of principal component analysis

ATo reduce the dimensionality of the data set

BTo identify new meaningful underlying variables

CTo discover the dimensionality of the data set

DOnly 1 and 2

EAll 1, 2 and 3
Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible: and each succeeding component accounts for as much of the remaining variability as possible.
Objectives of principal component analysis
1. To discover or to reduce the dimensionality of the data set.
2. To identify new meaningful underlying variables.

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Suggested Answer: E

by Estrella at Jul 06, 2025, 08:55 PM

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Ettie

2 days ago

I think the objectives include both reducing dimensionality and finding new underlying variables, so maybe D is correct?

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Gussie

8 days ago

I remember PCA is mainly about reducing dimensionality, but I'm not sure if it also identifies new variables.

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Kasandra

13 days ago

I feel pretty good about this question. The key objectives of PCA are to reduce the dimensionality of the data and to identify new meaningful variables that capture the essential information. I'll double-check my understanding, but I think I can nail this one.

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Val

19 days ago

Wait, I'm a bit confused. Is PCA just for dimensionality reduction, or does it also involve discovering the dimensionality of the data? I need to review my notes to make sure I understand the full scope of PCA's objectives.

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Trinidad

24 days ago

Okay, I've got this. PCA is used to reduce the number of variables in a dataset while retaining as much of the original information as possible. It also helps identify new underlying variables that capture the key patterns in the data. I'm confident I can select the right answer.

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Phillip

29 days ago

Hmm, I'm a bit unsure about the exact objectives here. I know PCA is used for dimensionality reduction, but I can't quite remember if it's also for discovering the dimensionality of the data. I'll have to think this through carefully.

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Larue

1 month ago

This looks like a straightforward question on the objectives of PCA. I'll focus on recalling the key points about dimensionality reduction and identifying new variables.

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Zona

3 months ago

I'm going with C. Discovering the dimensionality of the data set is the core of PCA, everything else is just a happy byproduct. Hey, at least it's not as confusing as quantum mechanics, right?

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Novella

2 months ago

A) To reduce the dimensionality of the data set

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Eun

3 months ago

E is the way to go! PCA covers all three key objectives - dimensionality reduction, finding new variables, and determining the dimensionality of the data. Gotta love a one-stop-shop solution!

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Nydia

3 months ago

Absolutely, E covers all the bases. PCA is a powerful tool for data analysis.

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Tasia

3 months ago

I agree, E is the most comprehensive option. PCA really does it all.

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Dominque

3 months ago

E is definitely the best choice. PCA covers all the key objectives in one go.

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Glendora

4 months ago

I believe the answer options A) To reduce the dimensionality of the data set and B) To identify new meaningful underlying variables are correct.

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Lore

4 months ago

I agree with you. PCA helps in simplifying the data while retaining important information.

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Merlyn

4 months ago

Definitely option D. PCA is all about reducing dimensionality and finding new underlying variables. The other options are just subsets of the main objectives.

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Xenia

3 months ago

Absolutely, option D captures the essence of principal component analysis perfectly. It's about reducing dimensions and revealing new insights.

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Yolande

3 months ago

Yes, PCA is all about simplifying the data and uncovering hidden patterns. Option D covers both objectives.

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Stephen

4 months ago

I agree, option D is the correct choice. PCA aims to reduce dimensionality and identify new variables.

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Frank

4 months ago

I think the correct objectives of principal component analysis are to reduce the dimensionality of the data set and to identify new meaningful underlying variables.

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