AI0-001 Machine Learning and Deep Learning Practice Question
A machine learning engineer notices that the gradient values in a deep network are becoming extremely small during backpropagation. What is this problem?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between vanishing and exploding gradients by describing the symptom (small vs. large gradients) and expects candidates to recognize that vanishing gradients cause slow learning in early layers, not just any training difficulty.
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
✓
Vanishing gradient
The vanishing gradient problem occurs when gradients become extremely small during backpropagation, especially in deep networks with many layers. This causes the weights in earlier layers to update very slowly or not at all, severely hindering training. The correct answer is D because the scenario directly describes the hallmark symptom of vanishing gradients.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Dead ReLU
Why it's wrong here
Dead ReLU describes neurons outputting zero for all inputs, giving zero gradient through that unit, not uniformly tiny gradients across the network. It is tempting because both involve small gradients, but dead ReLU is localised to specific rectified units rather than affecting backpropagation depth-wide.
- ✗
Exploding gradient
Why it's wrong here
Exploding gradients are the opposite problem: gradients grow exponentially, producing unstable, diverging updates. The term is tempting because both are gradient-scaling pathologies in deep networks, but vanishing gradients shrink towards zero, whereas exploding gradients exceed stable magnitudes.
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Covariate shift
Why it's wrong here
Covariate shift concerns a change in input feature distribution between training and inference, unrelated to gradient magnitude during backpropagation. It is tempting because it also degrades training, but it manifests as distribution mismatch, not gradients shrinking towards zero through successive layers.
- ✓
Vanishing gradient
Why this is correct
Repeated multiplication of small derivatives during backpropagation shrinks gradients exponentially with depth, so early layers barely update. This matches the stem's observation of extremely small gradient values, distinguishing it from exploding gradients, where values grow instead.
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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.