AWS Certified Machine Learning Engineer - Associate MLA-C01 — Topic 1 Question 12

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. The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model. Which action will meet this requirement with the LEAST operational overhead?

Accepted Answer

Correct answer: C. The correct answer is C. Option C: Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data. Amazon SageMaker builds, trains and deploys machine learning models in a fully managed service.

AWS Certified Machine Learning Engineer - Associate MLA-C01 — Question 12