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Amazon MLS-C01 Exam - Topic 9 Question 35 Discussion

A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or notWhich built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?
C) K-means
A) Seq2seq
B) XGBoost
D) Random Cut Forest (RCF)

Amazon MLS-C01 Exam - Topic 9 Question 35 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 35
Topic #: 9
[All MLS-C01 Questions]

A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not

Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

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Suggested Answer: C

by Taryn at May 08, 2022, 05:27 PM

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Bernadine
9 months ago
I agree, XGBoost has great performance on similar tasks!
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Rosamond
9 months ago
Wait, why not just use a simple logistic regression? Seems easier!
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Dominic
9 months ago
K-means? Not really suitable for this problem, right?
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Lewis
9 months ago
Random Cut Forest could work too, especially for anomaly detection.
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Danica
9 months ago
I think XGBoost is the way to go for this!
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Heike
10 months ago
Hmm, I'm not sure about this one. The options seem a bit confusing. I'll need to read through them carefully.
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Sarah
10 months ago
Hmm, I'm a bit unsure here. I'm trying to decide between A and C. Creating a new SingleBrandStore custom object might be cleaner, but adding the custom attribute to the existing Store object could also work. I'll have to think this through carefully.
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