MLS-C01 Modeling Practice Question
A financial services company is developing a fraud detection model using gradient boosting. The dataset contains 10 million transactions with 0.1% fraudulent. The model is trained on a SageMaker ml.m5.2xlarge instance and takes 8 hours. The team needs to reduce training time without sacrificing model performance. They have permission to use up to 4 instances. What should they do?
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
✓
Switch to a built-in XGBoost with GPU support and use a p3.2xlarge instance
GPU instances like p3.2xlarge accelerate XGBoost training substantially.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Switch to a built-in XGBoost with GPU support and use a p3.2xlarge instance
Why this is correct
GPU acceleration can significantly reduce training time for gradient boosting.
- ✗
Use SageMaker hyperparameter tuning to find faster hyperparameters
Why it's wrong here
Tuning adds overhead and does not guarantee faster training.
- ✗
Use SageMaker's distributed training with data parallelism across 4 ml.m5.2xlarge instances
Why it's wrong here
XGBoost does not natively support data parallelism; it uses all-reduce but may not scale linearly.
- ✗
Use SageMaker managed spot training with checkpointing
Why it's wrong here
Spot training reduces cost but not necessarily training time.
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