Databricks Certified Professional Data Scientist Exam - Topic 4 Question 54 Discussion

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

Question #: 54
Topic #: 4

[All Databricks Certified Professional Data Scientist Questions]

In which of the following scenario we can use naTve Bayes theorem for classification

AClassify whether a given person is a male or a female based on the measured features. The features include height, weight and foot size.

BTo classify whether an email is spam or not spam

CTo identify whether a fruit is an orange or not based on features like diameter, color and shape
naive Bayes classifiers have worked quite well in many real-world situations, famously document classification and spam filtering. They requires a small amount of training data to estimate the necessary parameters

Show Suggested Answer

Suggested Answer: C

by Carlee at Mar 07, 2024, 10:29 PM

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Shaun

4 months ago

For sure, B is the best example here!

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Elly

5 months ago

Naive Bayes is great for text data, but not sure about height and weight.

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Marg

5 months ago

Wait, can it really classify fruits? Sounds odd!

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Mickie

5 months ago

I think it can also be used for gender classification.

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Britt

5 months ago

Definitely works for spam classification!

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Nan

6 months ago

I feel like option A could work too, but I’m not confident if Naive Bayes is the best fit for gender classification based on physical features.

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Maurine

6 months ago

I practiced a similar question where we had to classify emails, and I think that aligns with option B since it’s a common use case for Naive Bayes.

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Angella

6 months ago

I'm not entirely sure, but I think Naive Bayes can also work for classifying fruits based on features, like in option C.

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Cory

6 months ago

I remember that Naive Bayes is great for text classification, especially for spam detection, so I think option B might be the right choice.

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Ruth

6 months ago

The key thing to remember is that Naive Bayes works best when the features are independent. So for classifying people as male or female, or fruits as oranges or not, that seems like a good fit. But email spam is a bit more complex, with things like word frequencies and other patterns, so I'm not sure Naive Bayes is the best approach there.

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Shaun

6 months ago

Okay, let's see. Naive Bayes assumes independence between features, and that the features follow a particular distribution. I think options A and C would work, since the physical characteristics like height, weight, and fruit properties seem to fit that model. But I'm not sure about the email spam classification in option B.

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Cecil

6 months ago

Hmm, I'm a bit unsure about this one. I know Naive Bayes is used for classification, but I'm not sure if all of these scenarios fit the assumptions. I'll need to think through the details carefully.

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Roxane

6 months ago

This looks like a straightforward application of Naive Bayes classification. I'm confident I can apply the concepts we learned in class to solve this.

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Martina

6 months ago

I'm a bit stumped on this one. The options seem to cover different organizational models, but I'm not familiar enough with the specifics to feel confident. I'll make an educated guess, but I may have to come back to this question if I have time at the end.

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Olen

6 months ago

Okay, let's see here. The key seems to be enabling token encryption for the registered app, App1. I'm not sure why that option is unavailable, so I'll need to investigate further.

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Quiana

6 months ago

This is a good question. I think the best approach is to go with option C and disallow duplicate invoice numbers on the A/P Constants form. That way, we can enforce the requirement at the system level and ensure users can't accidentally enter duplicates.

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Daren

11 months ago

Spam filtering with Naive Bayes? I bet the spammers are shaking in their boots!

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Lezlie

11 months ago

I'm a bit stumped on this one, to be honest. Maybe I need to brush up on my Naive Bayes theory. But I'm leaning towards C - those fruit features sound like a perfect fit!

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Peter

11 months ago

Ooh, this is a tricky one. I'm gonna go with B on this one. Can't go wrong with the classic spam filter use case for Naive Bayes, am I right?

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Valda

9 months ago

I agree, B) To classify whether an email is spam or not spam is a classic use case for Naive Bayes.

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Lili

10 months ago

Definitely B) To classify whether an email is spam or not spam. Naive Bayes works well for spam filtering.

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Virgina

10 months ago

I think A) Classify whether a given person is a male or a female based on the measured features. The features include height, weight and foot size.

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Omega

11 months ago

I think C is the correct answer. Classifying fruits based on measurable features like diameter, color, and shape is exactly the kind of problem Naive Bayes excels at.

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Stevie

10 months ago

C) To identify whether a fruit is an orange or not based on features like diameter, color and shape

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Ahmed

10 months ago

B) To classify whether an email is spam or not spam

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Johnetta

11 months ago

A) Classify whether a given person is a male or a female based on the measured features. The features include height, weight and foot size.

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Catarina

1 year ago

Definitely option B! Naive Bayes is a great fit for spam classification. It's fast, efficient, and can handle the high-dimensional feature space of emails.

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