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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?

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Suggested Answer: B

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

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Madelyn
1 day ago
Content-based filtering is too narrow for this. B is the best!
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Marge
6 days ago
C) Logistic Regression could work, but not here.
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Florinda
11 days ago
Definitely B! It’s all about community input.
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Rolland
17 days ago
I’m leaning towards A) Naive Bayes. But it feels off.
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Keneth
22 days ago
Hmm, I’m not convinced this is the best method.
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Graham
27 days ago
Totally agree with Collaborative filtering! Makes sense.
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Skye
2 months ago
Wait, are you sure it's not D) Content-based filtering?
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Barrett
2 months ago
I thought it was A) Naive Bayes at first.
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Lai
2 months ago
Definitely B) Collaborative filtering! That's how it works.
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Nakita
2 months 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
2 months 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
2 months 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
3 months 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
3 months 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
3 months 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
3 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
3 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
3 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
4 months ago
Agreed! Community votes shape the recommendations.
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Glenn
4 months ago
I think it's B) Collaborative filtering. Makes sense!
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Cassie
4 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
4 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
4 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
5 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
5 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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