MLS-C01 Modeling Practice Question
A financial services company is building a fraud detection model using a large dataset of credit card transactions. The dataset contains 10 million rows with 50 features, including transaction amount, merchant category, time of day, and customer historical features. The label is binary: fraudulent (1% of data) or legitimate. The company wants to deploy a real-time inference endpoint using Amazon SageMaker that can score transactions with sub-100ms latency. The current model is a gradient boosting model (XGBoost) trained on a sample of 1 million rows due to memory constraints. The model achieves 0.95 AUC on a held-out test set but the fraud recall (sensitivity) is only 0.4, which is unacceptable because the cost of missing a fraud is high. The data science team has access to a larger compute instance (ml.m5.24xlarge) for training. Which course of action is most likely to improve fraud recall while maintaining latency requirements?
⚠ Common exam trap
Many candidates choose downsampling (Option C) as a quick fix for class imbalance, overlooking that it discards valuable majority class data and can harm model generalization, while SMOTE (Option A) preserves data and synthetically balances the classes to improve recall without sacrificing latency.
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
✓
Train the XGBoost model on the full 10 million rows using an ml.p3.2xlarge instance with GPU support, and apply SMOTE oversampling to the minority class before training.
Training on the full 10 million rows with a GPU-accelerated instance (ml.p3.2xlarge) allows the XGBoost model to learn from the complete data distribution, addressing the bias introduced by the 1 million row sample. Applying SMOTE oversampling to the minority class (fraud) directly tackles the class imbalance (1% fraud), which is the root cause of the low recall (0.4). SMOTE generates synthetic fraudulent examples, improving the model's ability to detect fraud without significantly increasing inference latency, as the model architecture and deployment remain unchanged.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Train the XGBoost model on the full 10 million rows using an ml.p3.2xlarge instance with GPU support, and apply SMOTE oversampling to the minority class before training.
Why this is correct
Using a GPU instance allows training on the full dataset efficiently, and SMOTE oversampling balances the classes, directly improving recall.
- ✗
Engineer additional features from transaction time and merchant category, then retrain the XGBoost model on the same 1 million row sample.
Why it's wrong here
Feature engineering may help but does not address the data volume or class imbalance directly; the recall improvement may be insufficient.
- ✗
Downsample the majority class to 1% of the original size to create a balanced dataset of 200,000 rows, then retrain the XGBoost model on this balanced sample.
Why it's wrong here
Downsampling discards most of the data, which can harm model performance and reduce recall due to loss of information.
- ✗
Replace XGBoost with a logistic regression model trained on the full dataset, as linear models are faster to train and may generalize better on large data.
Why it's wrong here
Logistic regression is a linear decision boundary, whereas XGBoost captures non-linear interactions among the 50 features (e.g., transaction amount combined with merchant category) that are critical for detecting rare fraud patterns; replacing it would likely reduce recall further, not improve it. This option is tempting because logistic regression trains extremely fast on 10 million rows and is often used for low-latency scoring in real-time systems, but it sacrifices the complex feature interactions needed when fraud recall is the priority.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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