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

Limited Time Offer

25%

Off

Get Premium MLA-C01 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Ma

4 days 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.

upvoted 0 times

...