Question 914 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a model using SageMaker's built-in XGBoost algorithm. The dataset has 500 features and 1 million rows. The training job is taking too long. The scientist wants to reduce training time without sacrificing accuracy. Which action is LIKELY to be most effective?
⚠ Common exam trap
It's easy for candidates to assume scaling up instance size (Option C) is the best way to reduce training time, but they overlook that feature reduction (Option D) addresses the fundamental computational bottleneck of high dimensionality, which is more effective and cost-efficient.
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
✓
Apply Principal Component Analysis (PCA) to reduce the number of features
PCA reduces the dimensionality of the dataset from 500 features to a smaller set of principal components, which directly decreases the computational complexity of training the XGBoost model. With fewer features, each tree split requires fewer operations, and the overall training time drops significantly. Since PCA retains the variance in the data, it can preserve model accuracy while speeding up training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a smaller instance type to reduce time
Why it's wrong here
Smaller instance may increase training time.
- ✗
Reduce the number of trees in XGBoost
Why it's wrong here
Reducing trees may reduce accuracy.
- ✗
Use a larger instance type with more vCPUs
Why it's wrong here
Larger instance may speed up but increase cost.
- ✓
Apply Principal Component Analysis (PCA) to reduce the number of features
Why this is correct
PCA reduces dimensionality, speeding up training while retaining most information.
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Last reviewed: Jul 4, 2026
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