AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 208

A healthcare company wants to detect irregularities in patient vital signs that could indicate early signs of a medical condition. The company has an unlabeled dataset that includes patient health records, medication history, and lifestyle changes.

Which algorithm and hyperparameter should the company use to meet this requirement?

Answer options

• A. Use the Amazon SageMaker AI XGBoost algorithm. Set max_depth to greater than 100 to regulate tree complexity.
• B. Use the Amazon SageMaker AI k-means clustering algorithm. Set k to determine the number of clusters.
• C. Use the Amazon SageMaker AI DeepAR algorithm. Set epochs to the number of training iterations.
• D. Use the Amazon SageMaker AI Random Cut Forest (RCF) algorithm. Set num_trees to greater than 100.

Correct answer: D

Explanation

The correct answer is D because the Random Cut Forest (RCF) algorithm is specifically designed for anomaly detection and works well with unlabeled data. The other options, while valid algorithms, do not fit the requirement of detecting irregularities as effectively as RCF does in this context.