Amazon MLA-C01 Exam - 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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Juan

4 months ago

Option A seems too basic for this kind of task.

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Nida

4 months ago

QuickSight is great for visualization, but I’m not sure about using it with AWS Batch.

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Hyun

4 months ago

I disagree, I believe option B would work better with Redshift Spectrum.

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Kiley

5 months ago

I think option C is the best choice for anomaly detection and visualization.

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Ettie

5 months ago

Wait, can SageMaker really handle both detection and visualization? Sounds too good to be true!

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Lindsey

5 months ago

I think AWS Batch is more for processing jobs rather than anomaly detection, but I'm not completely confident about that.

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Honey

5 months ago

I practiced a similar question where QuickSight was used for visualization, but I can't recall if it was paired with Redshift or something else.

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Mabelle

5 months ago

I'm not entirely sure, but I feel like Amazon Athena is more for querying data rather than detecting anomalies.

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Tricia

6 months ago

I remember we discussed class imbalance in our study group, and I think using SageMaker might help with that.

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Mariko

6 months ago

Option C with Amazon SageMaker Data Wrangler could be a good all-in-one solution, but I'll need to research how well it handles the specific challenges mentioned in the case study.

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Desiree

6 months ago

The data is spread across S3 and an on-premises MySQL database, so I'll need to consider how each option handles that kind of hybrid data setup.

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Luis

6 months ago

I'm leaning towards option B - using Amazon Redshift Spectrum to detect the anomalies and Amazon QuickSight for visualization. That seems like a robust and flexible approach.

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Joseph

6 months ago

Hmm, the class imbalance and feature interdependencies in the data make this a bit tricky. I'll need to think through how each of the options might handle those challenges.

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Audria

7 months ago

This seems like a straightforward case study, but I'll need to carefully read through the details to identify the best solution. The key requirements are automatically detecting anomalies and visualizing the results.

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Jaclyn

9 months 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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Kattie

9 months ago

Definitely, efficiency is key in our line of work. Let's go with option C for this project.

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Ellsworth

9 months 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

9 months ago

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

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Lorean

10 months 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

10 months 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

10 months 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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Shawna

9 months ago

True, but I still think option D with AWS Batch and QuickSight could be a simpler solution for this case. It's worth considering.

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Gilma

9 months ago

I agree, but using Amazon Athena from option A might also be a good fit. It can handle large-scale data processing efficiently.

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Izetta

9 months 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

10 months ago

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

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Lorean

10 months 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

10 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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