AWS Certified Machine Learning – Specialty — Question 185

An ecommerce company wants to use machine learning (ML) to monitor fraudulent transactions on its website. The company is using Amazon SageMaker to research, train, deploy, and monitor the ML models.

The historical transactions data is in a .csv file that is stored in Amazon S3. The data contains features such as the user's IP address, navigation time, average time on each page, and the number of clicks for each session. There is no label in the data to indicate if a transaction is anomalous.

Which models should the company use in combination to detect anomalous transactions? (Choose two.)

Answer options

• A. IP Insights
• B. K-nearest neighbors (k-NN)
• C. Linear learner with a logistic function
• D. Random Cut Forest (RCF)
• E. XGBoost

Correct answer: A, D

Explanation

The correct models for detecting anomalous transactions are IP Insights and Random Cut Forest (RCF). IP Insights helps in identifying unusual patterns based on user IP behavior, while RCF is specifically designed for anomaly detection in streaming data. Other options like K-nearest neighbors, Linear learner, and XGBoost are not tailored for unsupervised anomaly detection without labeled data.