Databricks Certified Professional Data Scientist Exam - Topic 2 Question 51 Discussion

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

Question #: 51
Topic #: 2

[All Databricks Certified Professional Data Scientist Questions]

You are creating a regression model with the input income, education and current debt of a customer, what could be the possible output from this model.

ACustomer fit as a good

BCustomer fit as acceptable or average category

Cexpressed as a percent, that the customer will default on a loan

D1 and 3 are correct

E2 and 3 are correct
Regression is the process of using several inputs to produce one or more outputs. For example The input might be the income, education and current debt of a customer The output might be the probability, expressed as a percent that the customer will default on a loan. Contrast this to classification where the output is not a number, but a class.

Show Suggested Answer

Suggested Answer: B

by Jerry at Dec 23, 2023, 04:07 PM

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Evette

3 months ago

Not sure if income alone can predict defaults accurately.

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Shanice

3 months ago

I’m with you, C seems spot on!

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Jerlene

3 months ago

Wait, can it really predict defaults like that?

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Ma

4 months ago

Definitely think C is the most accurate output.

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Sanda

4 months ago

Sounds like a classic regression scenario!

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Delisa

4 months ago

I think option D could be correct too, since it includes both A and C, but I'm not confident about that.

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Terrilyn

4 months ago

I practiced a similar question where the output was about loan default probabilities, so I lean towards option C or E.

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Veda

4 months ago

I'm not entirely sure, but I feel like options A and B could also be valid outputs based on customer fit categories.

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Adolph

5 months ago

I remember discussing how regression models can predict probabilities, so I think option C makes sense.

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Bette

5 months ago

Hmm, I'm a bit unsure about the wording of the question. Is the output supposed to be a categorical classification (good/average/default) or a continuous probability value? I'll need to carefully read through the question and options to make sure I understand the expected output before proceeding.

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Yvonne

5 months ago

This looks pretty straightforward. The input variables are clearly defined, and the expected output is the probability of default, which is a typical regression problem. I'll focus on feature engineering, model selection, and evaluating the model's performance on the test set.

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Stevie

5 months ago

Okay, I think I've got this. The key is recognizing that we're dealing with a regression problem, not a classification. So the output should be the predicted probability of default, not a categorical label. I'll need to be careful with my model selection and validation to ensure accurate predictions.

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Beata

5 months ago

Hmm, I'm a bit unsure about this one. Is the output supposed to be a classification (good/average/default) or a continuous probability value? I'll need to think through the best way to model this and interpret the results.

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Ngoc

5 months ago

This seems like a straightforward regression problem. I'd start by identifying the input variables (income, education, current debt) and the expected output (probability of default). Then I'd focus on selecting the appropriate regression model and ensuring the data is properly prepared.

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Hillary

5 months ago

Okay, I think I've got a good handle on this. Option A seems like the most direct way to set up the two network interfaces using the launch template. I'll make sure to double-check the BYOIP pool ID.

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Tamra

5 months ago

Hmm, I'm a bit confused by the options here. I'll need to re-read the question and think through the different scenarios for escalation.

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Freeman

9 months ago

Where's the 'all of the above' option? Regression models these days can do everything short of making you a sandwich. Might as well cover all the bases.

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Sherita

9 months ago

I'm going with B. Expressing the customer as 'acceptable' or 'average' is way more useful than just a raw default probability. Gotta think like a banker, not a mathematician.

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Lili

8 months ago

I think D is the correct choice. It combines both categorizing customers and predicting default probability.

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Adell

8 months ago

I see your point, but I still think B is the way to go. It's more practical for decision-making.

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Claudio

8 months ago

I disagree, I think C is the most important. Knowing the probability of default is crucial for risk assessment.

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Nana

9 months ago

I think A is the best option. We want to categorize customers as good fits.

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Ora

10 months ago

Ha! The exam question is trying to trick us. The answer is clearly E - both the probability of default and the customer's risk category should be the output. Amateur mistake, question writer.

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Tanesha

8 months ago

Yeah, the exam question is trying to be sneaky. E is the correct answer for sure.

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Latosha

9 months ago

I agree, it's definitely E. The question is trying to trick us.

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Dominga

9 months ago

I think the answer is E, both the probability of default and the customer's risk category should be the output.

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Chu

10 months ago

I'm torn between C and D. Outputting the probability of default makes sense, but the model could also categorize the customer as 'good', 'average', or 'high risk'. Hmm, tough choice.

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Jose

9 months ago

I think both C and D could be correct, it depends on how you want to interpret the output.

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Kaitlyn

9 months ago

C) expressed as a percent, that the customer will default on a loan

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Clorinda

9 months ago

A) Customer fit as a good

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Lorean

10 months ago

I believe E) 2 and 3 are correct could also be a possible output, considering the input variables.

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Yvonne

10 months ago

I agree with Mi, but I also think D) 1 and 3 are correct could be a possible output.

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Mi

10 months ago

I think the possible output could be C) expressed as a percent, that the customer will default on a loan.

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Ashleigh

10 months ago

I think D) 1 and 3 are correct because customer fit as good and the probability of defaulting on a loan are both important factors to consider.

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Keva

11 months ago

I agree with Coral, C) seems like the most logical output based on the inputs provided.

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Nickolas

11 months ago

C seems to be the correct answer. The regression model would output the probability of the customer defaulting on a loan, which is a key metric for lenders.

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