Databricks Exam Databricks Certified Professional Data Scientist Topic 5 Question 84 Discussion

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

Question #: 84
Topic #: 5

[All Databricks Certified Professional Data Scientist Questions]

A bio-scientist is working on the analysis of the cancer cells. To identify whether the cell is cancerous or not, there has been hundreds of tests are done with small variations to say yes to the problem. Given the test result for a sample of healthy and cancerous cells, which of the following technique you will use to determine whether a cell is healthy?

ALinear regression

BCollaborative filtering

CNaive Bayes

DIdentification Test
In this problem you have been given high-dimensional independent variables like yes, no: 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

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Suggested Answer: C

by Chaya at Jun 13, 2025, 10:52 PM

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Kassandra

2 days ago

I feel like the identification test option is too vague. I wish we had more clarity on what that entails compared to the other methods.

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Dion

8 days ago

I think we practiced a similar question where we had to choose between logistic regression and support vector machines. I might lean towards SVM here.

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Kimbery

13 days ago

I remember we discussed using Naive Bayes for classification problems, but I'm not entirely sure if it's the best fit for this specific scenario.

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Corrina

19 days ago

Linear regression? Really? That doesn't seem like the right approach for a binary classification problem. I'd go with something like a support vector machine or random forest. Those tend to work well when you have a lot of features to work with.

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Alaine

24 days ago

Okay, I think I've got this. Since we're trying to determine whether a cell is healthy or cancerous based on test results, a supervised learning algorithm like naive Bayes or a decision tree classifier would be a good fit. I'll make sure to properly preprocess the data and split it into training and testing sets.

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Jolanda

29 days ago

Hmm, I'm a bit unsure here. There are a few different machine learning approaches that could work, but I'm not sure which one would be best for this specific problem. I might need to review my notes on the pros and cons of each method.

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Vanesa

1 month ago

This seems like a classic binary classification problem, so I'd probably start by considering techniques like logistic regression or naive Bayes. The key will be identifying the most relevant features from the test results.

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Madonna

4 months ago

I'm stumped. Maybe I should just go with the good ol' 'eenie, meenie, miney, moe' method. At least it's better than collaborative filtering, am I right?

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Hubert

2 months ago

User 2: Yeah, that could work. It's a common choice for classification problems.

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Nieves

3 months ago

User 1: I think Naive Bayes might be the way to go.

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Lyda

3 months ago

User 2: I'm leaning towards Support Vector Machines for this one.

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Cassie

3 months ago

User 1: I think Naive Bayes might be the way to go.

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Karina

4 months ago

Collaborative filtering? Are we trying to recommend cancer cells to each other now? Nah, this is clearly a supervised learning problem. Naive Bayes all the way!

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Ivette

4 months ago

Identification Test? What is this, a game show? I'd definitely go with one of the machine learning techniques mentioned. Probably Random Decision Forests for that extra bit of accuracy.

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Tayna

4 months ago

Linear regression? Really? That's not going to work for a binary classification problem like this. Gotta go with something more suited for that, like logistic regression or SVM.

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Hildred

3 months ago

Definitely, linear regression is not the best choice for determining whether a cell is healthy or cancerous.

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Eladia

4 months ago

I agree, logistic regression or SVM would be more appropriate for this type of problem.

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Noah

4 months ago

Linear regression is not suitable for binary classification, you should consider logistic regression or SVM instead.

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Evelynn

5 months ago

Hmm, I think Naive Bayes would be the way to go here. It's great for handling high-dimensional data and predicting binary outcomes. Plus, it's pretty straightforward to implement.

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