Microsoft Exam DP-100 Topic 5 Question 94 Discussion

Actual exam question for Microsoft's DP-100 exam

Question #: 94
Topic #: 5

[All DP-100 Questions]

You need to implement a model development strategy to determine a user's tendency to respond to an ad.

Which technique should you use?

AUse a Relative Expression Split module to partition the data based on centroid distance.

BUse a Relative Expression Split module to partition the data based on distance travelled to the event.

CUse a Split Rows module to partition the data based on distance travelled to the event.

DUse a Split Rows module to partition the data based on centroid distance.

Show Suggested Answer

Suggested Answer: C

by Rozella at Dec 22, 2023, 10:29 AM

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Julene

10 days ago

I'm torn between A and D. Both seem to use the Relative Expression Split module, which sounds promising. Maybe I'll flip a coin and go with whichever one lands face-up.

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Leota

11 days ago

I'm not sure. Maybe we should consider option C) Use a Split Rows module to partition the data based on distance travelled to the event as well.

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Laurel

12 days ago

I agree with Edmond. Using centroid distance can help us better understand user behavior.

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Edmond

13 days ago

I think we should use option A) Use a Relative Expression Split module to partition the data based on centroid distance.

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Dulce

14 days ago

I see both points, but I think option D might be more suitable for this specific scenario as it directly relates to the user's tendency to respond to an ad.

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Rasheeda

15 days ago

I disagree, I believe option D is better as it partitions the data based on centroid distance which is more relevant to the task.

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Edwin

19 days ago

Hmm, I think option D is the way to go. Splitting the data based on centroid distance sounds like the most relevant approach for determining user response tendencies.

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Rebbeca

4 days ago

User 1: I agree, option D seems like the best choice for this model development strategy.

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Trina

27 days ago

I think we should use option A, because centroid distance is a common technique for clustering data.

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