Quick Answer
The answer is Random Cut Forest (RCF). This algorithm is the best SageMaker algorithm for time series anomaly detection because it is specifically designed for unsupervised outlier detection on streaming and seasonal data, using an ensemble of random trees to isolate anomalies based on how quickly a data point can be separated from the rest of the structure. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of which built-in SageMaker algorithm handles unlabeled, seasonal time-series sensor data without requiring training labels—a common trap is choosing a supervised algorithm like XGBoost or a forecasting model like DeepAR, but RCF is the correct choice because it detects deviations in pattern without needing historical anomaly labels. A helpful memory tip: think of RCF as “Randomly Cutting the Forest” to quickly isolate the odd tree out, making it ideal for spotting outliers in streaming sensor data.
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Random Cut Forest (RCF)
RCF is a SageMaker built-in algorithm for anomaly detection, suitable for time-series data.
K-Means
Why wrong: K-Means can detect outliers but is not designed for time-series data.
DeepAR
Why wrong: DeepAR is for time-series forecasting, not anomaly detection.
XGBoost
Why wrong: XGBoost is for supervised learning; anomaly detection often requires unsupervised methods.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Common exam traps
The trap here is that 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.
Detailed technical explanation
RCF works by building an ensemble of random binary trees where each node splits the data based on a randomly chosen feature and threshold; anomalies are isolated closer to the root because they require fewer splits to separate, and the anomaly score is derived from the average path length across trees. In real-world industrial sensor data, RCF can adapt to seasonal patterns by using a sliding window approach to model recent behavior, making it robust to concept drift without retraining. A subtle behavior is that RCF assumes data is independent and identically distributed (i.i.d.), so for strongly autocorrelated time-series, preprocessing like differencing or using a rolling window is recommended to avoid false positives.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLS-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 MLS-C01 exam.
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