Databricks Certified Professional Data Scientist Exam - 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 ?

ANaive Bayes classifier

BCollaborative filtering

CLogistic Regression

DContent-based filtering
Another aspect of collaborative filtering systems is 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 help 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

Show Suggested Answer

Suggested Answer: B

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

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Marti

3 months ago

Not sure if this is really the best approach...

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Junita

3 months ago

Wait, so it gets better the more you use it? That's cool!

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Laura

3 months ago

Seems like a mix of both, but I agree with B.

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Ahmed

4 months ago

I thought it was content-based filtering at first.

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Eura

4 months ago

Definitely collaborative filtering!

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Kayleigh

4 months ago

I feel a bit confused; I thought logistic regression could also be used for recommendations, but this seems more about user similarities.

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Olive

4 months ago

This sounds familiar; I practiced a question about recommendation systems that emphasized user profiling, which makes me lean towards collaborative filtering.

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Cristal

4 months ago

I'm not entirely sure, but I remember something about content-based filtering focusing more on the user's own past activity rather than others.

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Adolph

5 months ago

I think this is related to collaborative filtering since it mentions analyzing the history of similar users.

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Tamekia

5 months ago

The question provides a lot of detail about how the recommendation engine works, so I feel pretty confident this is a collaborative filtering system. I'll select option B unless I see something that makes me doubt that.

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Lelia

5 months ago

Okay, I think I've got it. The question is describing a collaborative filtering system, where the recommendations are based on analyzing user profiles and similar user behavior. Option B looks like the best answer.

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Carey

5 months ago

I'm a bit confused by the wording of the question. It mentions user profiling and personalized recommendations, but doesn't explicitly state the type of recommendation engine. I'll need to think this through carefully.

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Leslee

5 months ago

This sounds like a collaborative filtering recommendation engine. The key details are about using user profiles and past activity to personalize recommendations for each user.

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Wava

5 months ago

I think the answer is B. Setting communication limits in the Admin seems like the best way to globally control the email volume across all regions.

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Arletta

5 months ago

I'm a bit confused on this one. Is the usermod command the only way to change a user's shell, or are there other options? I want to make sure I select the right answer.

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Dorathy

1 year ago

I'm going with B) Collaborative filtering. Sounds like a textbook definition of this technique.

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Lashawnda

1 year ago

Definitely B) Collaborative filtering. The question describes the core principles of this approach perfectly.

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Sage

1 year ago

It's fascinating how the system improves its model of each user over time with more data.

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Jose

1 year ago

Collaborative filtering is all about analyzing user activity to provide personalized recommendations.

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Jonell

1 year ago

I agree, Collaborative filtering is the right choice for this recommendation engine.

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Catina

1 year ago

I believe it's Collaborative filtering because it analyzes past activity of users with similar tastes.

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An

1 year 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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