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MLA-C01 Practice Question: A machine learning engineer is training a deep…
A machine learning engineer is training a deep learning model on SageMaker and notices that the training loss decreases rapidly in the first few epochs but then plateaus. The validation loss starts increasing after 10 epochs. Which action should the engineer take to improve generalization?
⚠ Common exam trap
AWS often tests the distinction between underfitting and overfitting symptoms, and the trap here is that candidates mistake a plateauing training loss for a need to increase model complexity or learning rate, when the rising validation loss clearly signals overfitting that early stopping can mitigate.
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
✓
Use early stopping with validation loss monitoring
Early stopping is the correct action because the validation loss increasing after 10 epochs while training loss continues to decrease is a classic sign of overfitting. By monitoring validation loss and halting training when it stops improving (e.g., using a patience parameter), the engineer prevents the model from memorizing noise in the training data, thereby improving generalization. SageMaker's built-in training job features or the `EarlyStopping` callback in frameworks like TensorFlow or PyTorch can implement this directly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add more layers to the model
Why it's wrong here
Adding layers increases model capacity, likely increasing overfitting.
- ✓
Use early stopping with validation loss monitoring
Why this is correct
Early stopping halts training when validation loss stops decreasing, reducing overfitting.
- ✗
Increase the learning rate
Why it's wrong here
A higher learning rate can cause the loss to diverge or skip minima, worsening overfitting.
- ✗
Decrease the batch size
Why it's wrong here
Smaller batch sizes can add noise but are not the primary solution for overfitting.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.