Databricks Certified Professional Data Scientist Exam - Topic 6 Question 88 Discussion

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

Question #: 88
Topic #: 6

[All Databricks Certified Professional Data Scientist Questions]

You are doing advanced analytics for the one of the medical application using the regression and you have two variables which are weight and height and they are very important input variables, which cannot be ignored and they are also highly co-related. What is the best solution for that?

AYou will take cube root of height

BYou will take square root of weight

CYou will take square of the height.

DYou would consider using BMI (Body Mass Index)
If multiple variables are highly co-related then it is better you consider using the either of the variable which correlates more (which is not in the given option) or go for the new variable which is a function of the both the variable in this case it could be BMI (Body Mass Index). Because it is a function of both weight and height as per the below formula. BMI = Weight/(Height * Height)

Show Suggested Answer

Suggested Answer: D

by Laticia at Aug 18, 2025, 06:37 AM

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Isidra

2 months ago

Really? I thought square root of weight was the way to go.

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Luis

2 months ago

Totally agree with D, BMI is super relevant here!

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Mose

2 months ago

I think A could work too, but not sure it's the best.

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Pura

3 months ago

Wait, why not just use both variables directly?

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Robt

3 months ago

D seems like the best choice, BMI is a solid metric!

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Graham

3 months ago

I feel like I might be mixing up the options, but I don't think just transforming height or weight alone would be enough to handle their correlation.

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Vicente

4 months ago

I practiced a similar question where we had to deal with correlated variables, and I think creating a new variable like BMI makes the most sense here.

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Lonna

4 months ago

I'm not entirely sure, but I think taking the square root of weight might not really solve the correlation problem.

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Vi

4 months ago

I remember discussing how multicollinearity can affect regression models, and I think using a derived variable like BMI could help address that issue.

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Janessa

4 months ago

I'm a bit confused on this one. Taking the cube root or square of the variables doesn't seem like the right approach, but I'm not entirely sure why. I'll need to review the concept of multicollinearity and see if using BMI is the best solution. Gotta be careful with my answer here.

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Harley

4 months ago

Okay, I think I've got this. The key here is to address the multicollinearity between weight and height. Using BMI, which is a derived variable, seems like the best solution. I'll make sure to explain my reasoning clearly in the exam.

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Una

4 months ago

Hmm, this is a good question. I'm leaning towards using BMI since it's a function of both weight and height, and that might help address the multicollinearity issue. I'll need to double-check the formula and make sure I understand it well.

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Theola

5 months ago

This seems like a tricky one. I'm not sure if taking the cube root or square of the variables is the right approach. I think I'll need to think through the concept of multicollinearity and consider using a derived variable like BMI.

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