MLS-C01 Modeling Practice Question

A company is building a machine learning model to detect anomalies in industrial sensor data. The data is time-series with seasonal patterns. The data scientist wants to use Amazon SageMaker to train a model. Which algorithm is most suitable for this task?

⚠ Common exam trap

Watch out — candidates often confuse unsupervised anomaly detection with supervised forecasting or classification, leading them to pick DeepAR or XGBoost, but RCF is the only algorithm among the options specifically built for unsupervised anomaly detection on streaming data without requiring labels.

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

✓

Random Cut Forest (RCF)

Random Cut Forest (RCF) is the most suitable algorithm because it is designed for unsupervised anomaly detection on streaming and time-series data. It works by constructing an ensemble of random trees that isolate anomalies based on how quickly a data point can be separated from the rest, making it effective for detecting outliers in sensor data with seasonal patterns without requiring labeled training data.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Random Cut Forest (RCF)
Why this is correct
RCF is a SageMaker built-in algorithm for anomaly detection, suitable for time-series data.
✗
K-Means
Why it's wrong here
K-Means can detect outliers but is not designed for time-series data.
✗
DeepAR
Why it's wrong here
DeepAR is for time-series forecasting, not anomaly detection.
✗
XGBoost
Why it's wrong here
XGBoost is for supervised learning; anomaly detection often requires unsupervised methods.

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MLS-C01 Modeling Practice Question

Why each option matters

Option-by-option breakdown

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