Amazon Exam MLA-C01 Topic 1 Question 3 Discussion

Actual exam question for Amazon's MLA-C01 exam

Question #: 3
Topic #: 1

Case study

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.

The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.

After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result.

Which solution will meet these requirements?

AUse Amazon Athena to automatically detect the anomalies and to visualize the result.

BUse Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.

CUse Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.

DUse AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.

Show Suggested Answer

Suggested Answer: C

by Harley at Jun 16, 2025, 09:01 PM

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Jaclyn

11 days ago

You know, as an ML engineer, I'm always looking for the path of least resistance. Option C seems to cover all the bases, and I don't have to worry about setting up a whole data warehouse. Gotta love that efficiency!

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Ellsworth

4 days ago

I agree, using SageMaker Data Wrangler will save us a lot of time and effort. It's the most efficient option.

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Lashandra

4 days ago

Option C seems like the best choice. Amazon SageMaker Data Wrangler can handle both anomaly detection and visualization.

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Lorean

15 days ago

That's a good point, but I still think option C is more suitable for this specific case study.

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Dallas

16 days ago

Hold up, did someone say 'class imbalance'? That's my jam! Option C sounds like it has the tools to handle that, plus the visualization. I'm feeling confident about this one.

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Arlie

18 days ago

Hmm, I'm not sure. Option B with Redshift Spectrum and QuickSight could also work, but it might be overkill for this use case. I'd need to dig deeper into the requirements to make a more informed decision.

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Izetta

7 days ago

I think option C with SageMaker Data Wrangler could be a good choice. It's designed for handling complex datasets.

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Von

18 days ago

I disagree, I believe option B is better as Amazon Redshift Spectrum can handle large datasets efficiently.

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Lorean

25 days ago

I think option C is the best choice because Amazon SageMaker Data Wrangler can handle the class imbalance and interdependencies.

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Ma

1 months ago

I think option C is the way to go. Amazon SageMaker Data Wrangler has all the tools to handle the data preprocessing and anomaly detection, plus the visualization capabilities to get the job done.

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Dominga

3 days ago

I would go with option B, using Amazon Redshift Spectrum and QuickSight seems like a solid choice.

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Carolann

6 days ago

I think option A could also work well, Amazon Athena is powerful for detecting anomalies.

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Vallie

1 months ago

I agree, option C seems like the most comprehensive solution for this case study.

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