AI0-001 AI Concepts and Techniques Practice Question
A research team is training a deep learning model for image classification using a small dataset of 1,000 labeled images. They are concerned about overfitting. Which combination of regularisation techniques would be MOST effective?
⚠ Common exam trap
AI0-001 often tests the misconception that any single regularisation technique is sufficient; the trap is choosing early stopping or batch normalisation as if they were equivalent to dropout plus weight decay, when the question asks for the most effective combination.
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
✓
Dropout with a rate of 0.5 and L2 regularisation
Dropout with a rate of 0.5 randomly deactivates half of the neurons during each training step, forcing the network to learn redundant representations and reducing co-adaptation. L2 regularisation adds a penalty proportional to the square of weights to the loss function, discouraging large weights and smoothing the model. Together they combat overfitting from two complementary angles — architectural stochasticity and weight magnitude control — which is the most effective combination among the options.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use early stopping without any other regularisation
Why it's wrong here
Early stopping halts training when validation loss rises but adds no penalty to weights, so 1,000 images still overfit. It tempts because it is cheap and effective alongside other methods, and would be correct as one component of a combined regularisation strategy.
- ✓
Dropout with a rate of 0.5 and L2 regularisation
Why this is correct
Dropout at 0.5 randomly deactivates half the units each pass, forcing redundant representations, while L2 regularisation penalises large weights. Combined, they constrain model capacity on the 1,000-image dataset, directly addressing the stem's overfitting concern more effectively than either alone.
- ✗
L1 regularisation and batch normalisation
Why it's wrong here
L1 drives weights to zero for sparsity, and batch normalisation stabilises activations; neither directly constrains model capacity the way dropout or weight decay do. It tempts because both are legitimate regularisers, and would be correct for feature selection or faster convergence.
- ✗
Increase learning rate and use momentum
Why it's wrong here
A higher learning rate and momentum accelerate optimisation but do not penalise complexity, so they can worsen overfitting on 1,000 images. It tempts because both speed convergence, and would be correct when training is slow or stuck in poor minima.
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 →
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.