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Databricks Exam Databricks-Certified-Professional-Data-Scientist Topic 5 Question 67 Discussion

Actual exam question for Databricks's Databricks-Certified-Professional-Data-Scientist exam
Question #: 67
Topic #: 5
[All Databricks-Certified-Professional-Data-Scientist Questions]

You are designing a recommendation engine for a website where the ability to generate more personalized recommendations by analyzing information from the past activity of a specific user, or the history of other users deemed to be of similar taste to a given user. These resources are used as user profiling and helps the site recommend content on a user-by-user basis. The more a given user makes use of the system, the better the recommendations become, as the system gains data to improve its model of that user. What kind of this recommendation engine is ?

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Suggested Answer: B

by Billy at Aug 24, 2024, 09:14 PM

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Dorathy
2 months ago
I'm going with B) Collaborative filtering. Sounds like a textbook definition of this technique.
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Lashawnda
2 months ago
Definitely B) Collaborative filtering. The question describes the core principles of this approach perfectly.
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Sage
29 days ago
It's fascinating how the system improves its model of each user over time with more data.
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Jose
1 months ago
Collaborative filtering is all about analyzing user activity to provide personalized recommendations.
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Jonell
1 months ago
I agree, Collaborative filtering is the right choice for this recommendation engine.
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Catina
2 months ago
I believe it's Collaborative filtering because it analyzes past activity of users with similar tastes.
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An
2 months ago
The correct answer is B) Collaborative filtering. This type of recommendation engine uses the history and preferences of similar users to make personalized recommendations.
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Brunilda
27 days ago
Yes, that's because the system gains more data to improve its model of the user.
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Ashanti
1 months ago
I've heard that the more a user interacts with the system, the better the recommendations become.
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Flo
1 months ago
That's correct! Collaborative filtering uses the history of similar users to make recommendations.
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Gregoria
2 months ago
I think the answer is B) Collaborative filtering.
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Tish
2 months ago
I'm not sure, but I think it could also be Content-based filtering.
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Krissy
2 months ago
I agree with Daisy, Collaborative filtering makes sense for personalized recommendations.
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Daisy
2 months ago
I think the recommendation engine described is Collaborative filtering.
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