AI0-001 AI Concepts and Techniques Practice Question
A developer is fine-tuning a large language model for a legal document summarization task. They notice that during training, the loss decreases rapidly in the first few epochs but then plateaus with high variance. Which hyperparameter adjustment is MOST likely to help stabilize training?
⚠ Common exam trap
CompTIA often tests the misconception that high variance in loss is always solved by increasing batch size or regularization, when in fact the immediate cause is often an overly aggressive learning rate that prevents convergence.
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
✓
Decrease the learning rate
A high-variance loss plateau after rapid initial convergence typically indicates that the learning rate is too large, causing the optimizer to overshoot the minima and oscillate. Decreasing the learning rate allows smaller, more stable weight updates, reducing variance and enabling smoother convergence.
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 L1 regularization
Why it's wrong here
L1 regularization introduces sparsity but does not directly stabilize training variance from learning rate.
- ✓
Decrease the learning rate
Why this is correct
A lower learning rate reduces gradient step sizes, stabilizing training and reducing variance.
- ✗
Increase the batch size
Why it's wrong here
Increasing batch size can reduce variance but may not address plateauing; it could also slow convergence.
- ✗
Increase the number of epochs
Why it's wrong here
More epochs may not help if the model is already overfitting or stuck; it doesn't address variance.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.