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Amazon MLS-C01 Exam - Topic 3 Question 124 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 124
Topic #: 3
[All MLS-C01 Questions]

[Modeling]

A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.

The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:

Based on the model evaluation results, why is this a viable model for production?

Show Suggested Answer Hide Answer
Suggested Answer: C

by Dona at Nov 24, 2025, 07:53 AM

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Cherrie
4 days ago
I agree with Alice, A is the best choice!
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Edison
10 days ago
I feel like accuracy is key here.
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Trinidad
15 days ago
Option C sounds good, false positives can be expensive.
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Adelle
20 days ago
But isn't precision important too?
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Maynard
25 days ago
Yeah, false negatives are less costly.
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Hayley
1 month ago
Not sure if 86% is good enough for production though.
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Rima
1 month ago
Totally agree, the cost of churn is way higher than incentives!
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Yvette
2 months ago
Wait, how can precision be less than accuracy? That seems off.
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Lottie
2 months ago
I think option C makes the most sense here.
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Sherly
2 months ago
Precision over accuracy, that's the way to go! Option D gets my vote.
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Mattie
2 months ago
Haha, this question is like a game of churn-and-burn! I'm going with option C, the one that makes the most financial sense.
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Corazon
2 months ago
This is a tough one. I'd go with option A, since the cost of false negatives is the key factor here, not just the overall accuracy.
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Margret
3 months ago
I'm not sure about this one. The precision being 86% seems more relevant than the overall accuracy, so I'm leaning towards option D.
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Yvette
3 months ago
Hmm, I think option C is the correct answer. The accuracy of 86% is good, and the cost of false positives is likely less than the cost of false negatives.
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Dylan
3 months ago
I feel like precision being higher than accuracy could indicate a good model, but I can't remember if that's always the case.
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Hector
3 months ago
If I recall correctly, false negatives can be more costly in churn prediction, so option A might be the right choice.
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Mollie
3 months ago
I think we practiced a question where we had to compare precision and accuracy, but I'm not sure which one is more important in this context.
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Bette
4 months ago
I remember discussing how accuracy alone isn't enough to determine model viability, especially with imbalanced classes.
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Abraham
4 months ago
I'm leaning towards option C. The question says the cost of churn is far greater than the cost of the incentive, so minimizing false negatives (where the model fails to identify a customer who will churn) is likely the priority.
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Florencia
4 months ago
I think option A makes the most sense.
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Tesha
4 months ago
The model's 86% accuracy is solid!
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Zena
5 months ago
Okay, let me think this through. The model is 86% accurate, which seems pretty good. But the question is asking specifically about the costs, so I need to consider the false positives and false negatives and how that impacts the business.
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Angelica
5 months ago
Hmm, I'm a bit confused about the difference between accuracy and precision. I'll need to review those concepts before deciding on the answer.
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Delmy
5 months ago
I think the key here is to focus on the cost implications of false positives vs false negatives. The question is asking why this is a viable model for production, so we need to consider the business impact.
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Sharee
4 months ago
I agree, the cost implications are crucial.
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Portia
5 months ago
Yes, false negatives can be more costly in this scenario.
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