CertNexus AIP-210 Exam - Topic 7 Question 4 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 4
Topic #: 7

[All AIP-210 Questions]

Which of the following metrics is being captured when performing principal component analysis?

AKurtosis

BMissingness

CSkewness

DVariance

Show Suggested Answer

Suggested Answer: D

Principal component analysis (PCA) is a technique that reduces the dimensionality of a dataset by transforming it into a set of new variables called principal components. The principal components are linear combinations of the original variables that capture the maximum amount of variance in the data. The first principal component explains the most variance, the second principal component explains the second most variance, and so on. The goal of PCA is to retain as much variance as possible while reducing the number of variables.

by Devora at Oct 03, 2023, 05:51 AM

Limited Time Offer

25%

Off

Get Premium AIP-210 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Claudio

8 months ago

Definitely variance, no doubt about it!

upvoted 0 times

...

Lashandra

9 months ago

I thought kurtosis was involved too?

upvoted 0 times

...

Franklyn

9 months ago

Wait, isn't it also about skewness?

upvoted 0 times

...

Ocie

9 months ago

Totally agree, it's all about variance.

upvoted 0 times

...

Charlie

9 months ago

PCA captures variance!

upvoted 0 times

...

Lindsey

10 months ago

I vaguely recall that PCA is related to variance, but I’m not confident if it’s the primary metric. It could be a trick question!

upvoted 0 times

...

Lamar

10 months ago

I feel like kurtosis and skewness are more about distribution shapes, not what PCA measures. Variance seems right, but I’m a bit hesitant.

upvoted 0 times

...

Candida

10 months ago

I remember practicing a question about PCA and how it reduces dimensionality by maximizing variance. So, I’d lean towards option D.

upvoted 0 times

...

Angella

10 months ago

I think PCA focuses on variance, but I'm not entirely sure if that's the only thing it captures.

upvoted 0 times

...

Vincenza

10 months ago

I'm a little confused on this one. Is PCA related to the shape of the data distribution, like skewness or kurtosis? Or is it more about the spread of the data, which would be variance? I'll have to review my notes to be sure.

upvoted 0 times

...

Amina

10 months ago

Hmm, I'm a bit unsure about this one. I know PCA has something to do with reducing dimensionality, but I can't quite remember which specific metric it's capturing. I'll have to think this through carefully.

upvoted 0 times

...

Lynette

10 months ago

I'm pretty sure principal component analysis is about finding the directions of maximum variance in the data, so I think the answer is D. Variance.

upvoted 0 times

...

Benedict

10 months ago

Okay, let me see. PCA is all about finding the principal components that explain the most variability in the data. So the metric it's capturing must be variance. I'm confident that D is the right answer here.

upvoted 0 times

...

Sanjuana

10 months ago

I'm pretty confident about this one. The key is to focus on the specific functions mentioned in the question and evaluate which statement best describes the DTS database migration capabilities.

upvoted 0 times

...

Robt

10 months ago

Okay, let me think this through step-by-step. The question is asking about integrating BPM Suite and BAM 12c, so I need to look for an option that mentions configuring something related to that integration. I think option C, setting the DisableProcessMetricsMBean on the BAM server, sounds like the right approach.

upvoted 0 times

...