hardMultiple SelectObjective-mapped
AIF-C01 Practice Question: A machine learning team is building a binary…
A machine learning team is building a binary classifier using Amazon SageMaker. The dataset has 10,000 features and 1,000 samples. The model overfits severely. Which TWO approaches are MOST likely to reduce overfitting? (Choose two.)
⚠ Common exam trap
AWS often tests the misconception that increasing batch size or training longer always improves generalization, when in fact these techniques can worsen overfitting in high-dimensional, low-sample scenarios.
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
✓
Perform feature selection to reduce the number of features
With 10,000 features and only 1,000 samples, the model has a very high-dimensional feature space, which is a classic cause of overfitting. Reducing the number of features through feature selection lowers the model's capacity and removes noise, directly addressing the curse of dimensionality. In SageMaker, this can be done using built-in algorithms like XGBoost's feature importance or by preprocessing with PCA in a SageMaker Processing job.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the batch size to the full dataset
Why it's wrong here
Increasing batch size may have minor regularization effect but is not a primary approach to reduce overfitting.
- ✗
Use a neural network with more layers
Why it's wrong here
More layers increase model capacity, likely worsening overfitting.
- ✓
Perform feature selection to reduce the number of features
Why this is correct
Reducing features reduces dimensionality and overfitting risk.
- ✓
Add L2 regularization to the loss function
Why this is correct
L2 regularization penalizes large weights, reducing overfitting.
- ✗
Train the model for more epochs
Why it's wrong here
More epochs typically increase overfitting as the model memorizes training data.
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