MLA-C01 · Question #11
MLA-C01 Question #11: Real Exam Question with Answer & Explanation
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Question
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?
Options
- 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
- CUse Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize
- DUse AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the
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