hardMultiple Choice
AIF-C01 Practice Question: A team is building a real-time anomaly detection…
A team is building a real-time anomaly detection system for IoT sensor data. The data is unlabeled, and the team expects the anomalies to be rare but of high importance. Which combination of approach and AWS service should the team use?
⚠ Common exam trap
AWS often tests the distinction between supervised, unsupervised, and semi-supervised learning in the context of unlabeled data, and the trap here is that candidates may choose k-means clustering (option C) thinking any unsupervised method works, but k-means is not optimized for rare anomaly detection and lacks the one-class modeling that Lookout for Equipment provides.
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
✓
Use a semi-supervised one-class classifier with Amazon Lookout for Equipment
Amazon Lookout for Equipment is purpose-built for anomaly detection on unlabeled industrial sensor data. It uses a semi-supervised one-class classifier that learns the normal operating envelope from historical data and flags rare, high-importance anomalies in real time, matching the exact requirements of the scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Amazon Rekognition Custom Labels to detect anomalies in sensor images
Why it's wrong here
Amazon Rekognition is for image and video analysis, not IoT sensor time-series data.
- ✗
Train a supervised classifier using Amazon SageMaker; use Amazon Kinesis Data Analytics for real-time inference
Why it's wrong here
Supervised learning requires labeled data, which is not available. Also, Kinesis Data Analytics is for SQL-based analytics, not ML inference.
- ✗
Apply k-means clustering with Amazon SageMaker; deploy the model as a real-time endpoint
Why it's wrong here
K-means is an unsupervised method that does not explicitly model rare anomalies; it groups data into clusters and may not effectively detect rare events.
- ✓
Use a semi-supervised one-class classifier with Amazon Lookout for Equipment
Why this is correct
Amazon Lookout for Equipment trains one-class models on normal sensor telemetry alone, flagging deviations without labelled anomaly examples — matching the unlabeled, rare-anomaly constraint. It ingests multivariate time-series data directly from IoT sensors, so the semi-supervised one-class approach fits the scenario's requirement for detecting high-importance, infrequent faults.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-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 AIF-C01 exam.