MLA-C01 ML Model Development Practice Question
Which SageMaker built-in algorithm is best suited for detecting anomalous login attempts based on IP addresses and user behavior?
⚠ Common exam trap
MLA-C01 often tests whether candidates can distinguish purpose-built SageMaker algorithms (like IP Insights for anomaly detection) from general-purpose algorithms (XGBoost, PCA, K-Means) that require custom feature engineering for the same task.
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 SageMaker built-in algorithm purpose-built for learning the patterns of IP address usage by users and resources, making it ideal for detecting anomalous login attempts. It uses a neural network to embed IP addresses and entities (like user IDs) into a vector space, flagging unusual combinations as anomalies. XGBoost, PCA, and K-Means are general-purpose algorithms not designed for IP-entity behavioral analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
XGBoost
Why it's wrong here
XGBoost is supervised classification or regression, requiring labelled normal and anomalous examples that this scenario does not provide. It tempts because it performs well on tabular data, but IP Anomaly Detection handles unlabelled login behaviour instead.
- ✓
IP Insights
Why this is correct
IP Insights learns associations between IP addresses and user identities, flagging logins from unusual IP-user pairings. This directly satisfies the scenario's need to detect anomalous login attempts from IP and behaviour patterns, unlike supervised classification algorithms requiring labelled fraud data.
- ✗
PCA
Why it's wrong here
PCA reduces dimensionality for visualisation and feature compression; it does not output anomaly scores for individual login events. It tempts as an unsupervised technique, but IP Anomaly Detection is the built-in algorithm designed for this detection.
- ✗
K-Means
Why it's wrong here
K-Means partitions data into clusters by distance, so it cannot flag individual login events as anomalous. It tempts for grouping similar behaviour, but IP Anomaly Detection, an unsupervised algorithm, is built for exactly this detection task.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.