AIF-C01 Fundamentals of AI and ML Practice Question
During a SageMaker training job, the data scientist observes that the loss is not decreasing after the initial few epochs. The model is a deep neural network with ReLU activations. Which hyperparameter adjustment is most likely to help?
⚠ Common exam trap
The AIF-C01 exam often tests the misconception that increasing the learning rate will accelerate convergence, but in plateau scenarios it actually causes divergence or oscillation, making the reduction of learning rate the correct adjustment.
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
✓
Reduce the learning rate
When loss plateaus after a few epochs with ReLU activations, the model is likely stuck in a region where gradients are small (e.g., near a local minimum or plateau). Reducing the learning rate allows the optimizer to take smaller steps, which can help it navigate out of flat regions and continue decreasing the loss. This is a standard technique to improve convergence when training stalls.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Reduce the learning rate
Why this is correct
A lower learning rate can allow the optimizer to find a better minimum.
- ✗
Increase the number of epochs
Why it's wrong here
If loss is not decreasing, more epochs won't help.
- ✗
Increase the learning rate
Why it's wrong here
Increasing learning rate may cause divergence.
- ✗
Decrease the batch size
Why it's wrong here
Decreasing batch size can introduce noise but may not directly address plateauing loss.
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