Databricks Exam Databricks Certified Professional Data Scientist 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)

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Suggested Answer: D

by Laticia at Aug 18, 2025, 07:37 AM

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Vicente

2 days 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

8 days 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

13 days 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

18 days 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

23 days 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

28 days 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

1 month 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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Jennie

2 months ago

Hmm, I'm not sure about the other options. Cube root of height or square root of weight don't really make sense in this context.

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Delmy

3 months ago

D is the obvious choice here. BMI takes into account both weight and height, and it's a well-established metric in the medical field.

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Marya

2 months ago

Yes, BMI is a reliable indicator for assessing weight in relation to height. It's commonly used in medical applications.

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Kate

2 months ago

D is a good choice. BMI is a comprehensive measure that considers both weight and height.

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