Amazon MLS-C01 Exam - Topic 9 Question 35 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 35
Topic #: 9

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?

ASeq2seq

BXGBoost

CK-means

DRandom Cut Forest (RCF)

Show Suggested Answer

Suggested Answer: C

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

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Bernadine

4 months ago

I agree, XGBoost has great performance on similar tasks!

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Rosamond

4 months ago

Wait, why not just use a simple logistic regression? Seems easier!

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Dominic

4 months ago

K-means? Not really suitable for this problem, right?

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Lewis

4 months ago

Random Cut Forest could work too, especially for anomaly detection.

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Danica

5 months ago

I think XGBoost is the way to go for this!

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Heike

5 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

5 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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