Databricks Certified Professional Data Scientist Exam - Topic 4 Question 91 Discussion

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

Question #: 91
Topic #: 4

[All Databricks Certified Professional Data Scientist Questions]

Question-34. Stories appear in the front page of Digg as they are "voted up" (rated positively) by the community. As the community becomes larger and more diverse, the promoted stories can better reflect the average interest of the community members. Which of the following technique is used to make such recommendation engine?

ANaive Bayes classifier

BCollaborative filtering

CLogistic Regression

DContent-based filtering
One scenario of collaborative filtering application is to recommend interesting or popular information as judged by the community. As a typical example, stories appear in the front page of Digg as they are 'voted up' (rated positively) by the community. As the community becomes larger and more diverse, the promoted stories can better reflect the average interest of the community members.

Show Suggested Answer

Suggested Answer: B

by Izetta at Nov 20, 2025, 06:46 PM

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Skye

9 hours ago

Wait, are you sure it's not D) Content-based filtering?

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Barrett

6 days ago

I thought it was A) Naive Bayes at first.

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Lai

11 days ago

Definitely B) Collaborative filtering! That's how it works.

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Nakita

16 days ago

Haha, I bet the Digg engineers are just using a random number generator to decide what shows up on the front page. That would be a real plot twist!

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Mari

21 days ago

Naive Bayes? Really? That's more for text classification, not community-driven recommendations. Collaborative filtering is the obvious choice here.

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Giovanna

26 days ago

Content-based filtering? Nah, that's too narrow. Digg needs to capture the diverse interests of its growing community.

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Blondell

1 month ago

Hmm, I'm not sure. Logistic regression could also work, but it might be overkill for a simple voting system like Digg's.

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Jolene

1 month ago

Collaborative filtering is definitely the way to go for Digg's recommendation engine. Gotta love those community-driven upvotes!

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Aleisha

1 month ago

I feel like content-based filtering is more about individual preferences rather than community votes, so I lean towards B) Collaborative filtering for this one.

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Paris

2 months ago

I’m a bit confused. Could it also be A) Naive Bayes? I know it’s used for classification, but I’m not sure how it relates to community voting.

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Pa

2 months ago

I think the answer might be B) Collaborative filtering since it involves community ratings, but I'm not entirely sure.

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Karl

2 months ago

Collaborative filtering is the way to go here. The key is that Digg is using the community's collective input to determine what stories get promoted, rather than relying on the content itself. That's the essence of a collaborative filtering recommendation system.

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Clemencia

2 months ago

Agreed! Community votes shape the recommendations.

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Glenn

2 months ago

I think it's B) Collaborative filtering. Makes sense!

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Cassie

2 months ago

I'm a bit confused on the difference between collaborative filtering and content-based filtering. Can someone help me understand how they work and when each one would be more appropriate? I want to make sure I have a good grasp of these concepts.

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Pamela

3 months ago

Collaborative filtering is definitely the right answer. The question is asking about how Digg's system works, and that's all about using the community's votes and ratings to surface popular content. Seems pretty straightforward to me.

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Lindsey

3 months ago

I remember practicing a similar question, and I think it was about how user preferences influence recommendations. Collaborative filtering seems to fit that.

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Ben

3 months ago

Hmm, I'm a little unsure about this one. Collaborative filtering makes sense, but I'm also wondering if content-based filtering could be used to recommend stories based on their actual content and topics. I'll have to think this through a bit more.

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Giovanna

3 months ago

I think collaborative filtering is the way to go here. The question is specifically asking about how Digg's recommendation engine works, and that's a classic example of collaborative filtering.

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