The answer is to use Amazon SageMaker Random Cut Forest (RCF) for anomaly detection. This is correct because RCF is an unsupervised algorithm designed specifically for handling imbalanced data for fraud detection with Random Cut Forest, as it isolates rare events by counting the number of random partitions needed to separate a data point from the rest—fraudulent transactions, being anomalous, require fewer cuts and thus receive higher anomaly scores. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that supervised models like gradient boosting fail on extreme imbalance (0.1% fraud) despite high accuracy, while RCF bypasses the need for balanced training data and allows threshold tuning to catch 80% of fraud with minimal false positives. A common trap is choosing oversampling or cost-sensitive boosting, but RCF’s unsupervised nature is the key for anomaly detection in fraud. Memory tip: “Random Cuts Catch Rare Crooks”—fewer cuts mean more suspicious.
MLS-C01 Modeling Practice Question
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 financial services company is developing a fraud detection model using a highly imbalanced dataset where fraudulent transactions are only 0.1% of the data. The data scientist has trained a gradient boosting model that achieves 99.9% accuracy but only detects 20% of actual fraud cases. The business requirement is to detect at least 80% of fraud while minimizing false positives. The data scientist has access to SageMaker and can use any built-in algorithm or custom script. Which approach should the data scientist take to meet the business requirement?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "least"
Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Amazon SageMaker Random Cut Forest (RCF) algorithm for anomaly detection.
Amazon SageMaker Random Cut Forest (RCF) is an unsupervised anomaly detection algorithm that is well-suited for highly imbalanced datasets like this one (0.1% fraud). Unlike supervised methods that struggle with extreme class imbalance, RCF isolates anomalies by measuring how many random cuts are needed to separate a point from the rest of the data, making it effective at detecting rare fraud cases without requiring balanced training data. This approach can meet the 80% fraud detection requirement while minimizing false positives by tuning the anomaly score threshold.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Keep the model but adjust the classification threshold to increase recall.
Why it's wrong here
Adjusting threshold may increase recall but at the cost of many false positives, which is not acceptable.
✗
Use random under-sampling of the majority class to balance the dataset and retrain the model.
Why it's wrong here
Under-sampling discards valuable data and can hurt model performance.
✓
Use Amazon SageMaker Random Cut Forest (RCF) algorithm for anomaly detection.
Why this is correct
RCF is designed for anomaly detection on highly imbalanced data and can detect fraud effectively.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use random oversampling of the minority class to balance the dataset and retrain the model.
Why it's wrong here
Oversampling can cause overfitting and may not improve recall to 80%.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume a supervised model with threshold tuning (Option A) can solve the imbalance, but they overlook that the model's learned decision boundary is fundamentally biased, and unsupervised anomaly detection like RCF is specifically designed for such extreme imbalance scenarios.
Detailed technical explanation
How to think about this question
Random Cut Forest works by building an ensemble of trees where each tree is constructed by randomly selecting a feature and a split value; anomalies require fewer splits to isolate because they are sparse and distinct. The anomaly score is derived from the path length in the tree, and the algorithm can be scaled using SageMaker's distributed training for large datasets. In practice, RCF is often used for real-time fraud detection because it can process streaming data and adapt to concept drift without retraining on the full dataset.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Amazon SageMaker Random Cut Forest (RCF) algorithm for anomaly detection. — Amazon SageMaker Random Cut Forest (RCF) is an unsupervised anomaly detection algorithm that is well-suited for highly imbalanced datasets like this one (0.1% fraud). Unlike supervised methods that struggle with extreme class imbalance, RCF isolates anomalies by measuring how many random cuts are needed to separate a point from the rest of the data, making it effective at detecting rare fraud cases without requiring balanced training data. This approach can meet the 80% fraud detection requirement while minimizing false positives by tuning the anomaly score threshold.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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