AI0-001 AI Concepts and Techniques Practice Question
A machine learning engineer is training a neural network for image classification. The training loss decreases slowly and the model accuracy improves only marginally each epoch. Which hyperparameter adjustment is MOST likely to accelerate convergence?
⚠ Common exam trap
CompTIA AI often tests the misconception that adding more layers or increasing batch size always improves training speed, when in fact the learning rate is the primary hyperparameter controlling convergence rate.
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
✓
Increase the learning rate
The training loss decreasing slowly and accuracy improving marginally each epoch indicates that the learning rate is too small, causing the optimizer to take very small steps toward the minimum of the loss function. Increasing the learning rate allows the optimizer to take larger steps per update, which accelerates convergence. Option C is correct because adjusting the learning rate directly addresses the step size in gradient descent.
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 hidden layers
Why it's wrong here
Adding hidden layers increases model capacity, which typically slows training and can worsen convergence when loss already falls slowly. It is tempting because deeper networks fit complex image data, but that suits underfitting with ample compute and data, not a run whose per-epoch progress is marginal.
- ✗
Increase the batch size
Why it's wrong here
A larger batch size reduces the number of weight updates per epoch, so convergence per epoch usually slows rather than accelerates. It is tempting because large batches improve hardware throughput, but that is a training-speed benefit, not the per-epoch convergence improvement the scenario requires.
- ✓
Increase the learning rate
Why this is correct
Raising the learning rate increases the step size taken along the loss gradient, so each epoch moves weights further and convergence accelerates. The stem's slow loss decrease and marginal accuracy gains indicate steps that are too small, making a higher rate the direct fix.
- ✗
Decrease the number of epochs
Why it's wrong here
Reducing epochs stops training earlier, so the slowly falling loss simply halts before convergence; it cannot accelerate it. The adjustment is tempting when overfitting appears, where fewer epochs help, but here the model is underfitting, so raising the learning rate is the relevant change.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.