MLA-C01 ML Model Development Practice Question
A team is building a fraud detection model using SageMaker and wants to detect anomalies in user login events. Which SageMaker built-in algorithm is specifically designed for anomaly detection in event-based data?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
IP Insights
IP Insights is a built-in algorithm for learning representations of IP addresses and detecting anomalous login patterns, commonly used for fraud detection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Factorisation Machines
Why it's wrong here
Factorisation Machines are for recommendation systems with sparse data.
- ✓
IP Insights
Why this is correct
IP Insights is designed for anomaly detection on IP addresses and events.
- ✗
Random Cut Forest
Why it's wrong here
Random Cut Forest is for numeric anomaly detection, not specific to IP events.
- ✗
K-Means
Why it's wrong here
K-Means is for clustering, not anomaly detection.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.