AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 91

A company has an ML model that uses historical transaction data to predict customer behavior. An ML engineer is optimizing the model in Amazon SageMaker to enhance the model's predictive accuracy. The ML engineer must examine the input data and the resulting predictions to identify trends that could skew the model's performance across different demographics.

Which solution will provide this level of analysis?

Answer options

• A. Use Amazon CloudWatch to monitor network metrics and CPU metrics for resource optimization during model training.
• B. Create AWS Glue DataBrew recipes to correct the data based on statistics from the model output.
• C. Use SageMaker Clarify to evaluate the model and training data for underlying patterns that might affect accuracy.
• D. Create AWS Lambda functions to automate data pre-processing and to ensure consistent quality of input data for the model.

Correct answer: C

Explanation

The correct answer is C because SageMaker Clarify is specifically designed to analyze the model and the training data for biases and trends that could impact the model's performance. Options A and D focus on monitoring and automation, which do not directly address the need for analysis of demographic trends. Option B, while useful for data correction, does not provide the comprehensive analysis required to evaluate underlying patterns affecting accuracy.