Databricks Certified Professional Data Scientist Exam - Topic 1 Question 87 Discussion

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

Question #: 87
Topic #: 1

[All Databricks Certified Professional Data Scientist Questions]

Suppose you have been given a relatively high-dimension set of independent variables and you are asked to come up with a model that predicts one of Two possible outcomes like "YES" or "NO", then which of the following technique best fit.

ASupport vector machines

BNaive Bayes

CLogistic regression

DRandom decision forests

EAll of the above
In this problem you have been given high-dimensional independent variables like yeS; nO; no English words , test results etc. and you have to predict either valid or not valid (One of two). So all of the below technique can be applied to this problem.
* Support vector machines
* Naive Bayes
* Logistic regression
* Random decision forests

Show Suggested Answer

Suggested Answer: E

by Haydee at Aug 04, 2025, 07:38 AM

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Evangelina

2 months ago

Random forests handle high dimensions well, so I agree with E!

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Eleni

2 months ago

Wait, can all of them really work for this? Sounds too good to be true!

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Stephane

2 months ago

Logistic regression is a classic choice for binary outcomes.

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Myra

2 months ago

Naive Bayes is super simple and effective too.

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Brinda

3 months ago

I think Support Vector Machines are great for high dimensions!

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Lorrie

3 months ago

Random decision forests seem like a solid option since they can manage high-dimensional data well, but I’m torn between that and support vector machines.

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Nana

4 months ago

I practiced a similar question where logistic regression was highlighted as a go-to for binary outcomes, but I wonder if it handles high dimensions as well as others.

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Lauran

4 months ago

I think Naive Bayes could work too, especially since it assumes independence among features, but I’m not confident about its performance with high dimensions.

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Glen

4 months ago

I remember we discussed support vector machines being effective for high-dimensional data, but I'm not entirely sure if it's the best choice here.

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Keneth

4 months ago

All of the options listed could work, but I'd probably lean towards naive Bayes. It's a simple and fast algorithm that can handle high-dimensional data, and it might be a good fit for this binary classification task.

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Samira

4 months ago

Okay, I think I've got this. Logistic regression or support vector machines would both be good choices here. They can handle the high-dimensional data and binary outcome. I'll have to review the details of each to decide which one to use.

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Delpha

4 months ago

Hmm, I'm not sure which one to pick. Support vector machines and random forests both seem like good options for this type of problem. I'll have to think through the pros and cons of each.

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Nikita

5 months ago

This looks like a classic binary classification problem. I'd probably start with logistic regression since it's a simple and robust model that can handle high-dimensional data.

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