AI0-001 Machine Learning and Deep Learning Practice Question
An organization wants to automate the detection of defective products on an assembly line using computer vision. They have a limited number of labeled images for defective items. Which approach would be most effective?
⚠ Common exam trap
CompTIA often tests the misconception that more data (via GANs) is always better, or that starting from scratch is necessary for a new task, when in reality transfer learning is the standard solution for small datasets in computer vision.
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
✓
Use transfer learning with a pre-trained model like ResNet and fine-tune on the defect data
Transfer learning with a pre-trained model like ResNet is most effective because it leverages features learned from large datasets (e.g., ImageNet) and adapts them to the defect detection task with limited labeled data. Fine-tuning only the later layers preserves general visual features while specializing for defect classification, avoiding overfitting that would occur with a small dataset.
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 a support vector machine with handcrafted features
Why it's wrong here
Handcrafted features require domain expertise and cannot learn the subtle defect patterns that scarce labelled examples must convey; a CNN with transfer learning exploits pretrained representations instead. SVMs suit small, tabular, linearly separable datasets with engineered features, not image data where labelled defects are limited.
- ✗
Train a convolutional neural network from scratch on the limited data
Why it's wrong here
Training a convolutional network from scratch on scarce labelled defects causes overfitting, since millions of parameters cannot be learned from so few examples. It is tempting because CNNs are the standard architecture for image tasks, and from-scratch training would be correct given a large, balanced labelled dataset.
- ✗
Synthesize additional defective images using GANs
Why it's wrong here
Generating synthetic data via Generative Adversarial Networks (GANs) often introduces artifacts that fail to capture the specific textural irregularities required for high-precision defect detection. While this technique serves well when augmenting datasets to improve model robustness against environmental variance, it does not address the fundamental lack of ground-truth labels needed for supervised learning in this scenario. Data augmentation or transfer learning provides the necessary label alignment that GAN-generated imagery lacks.
- ✓
Use transfer learning with a pre-trained model like ResNet and fine-tune on the defect data
Why this is correct
Transfer learning reuses features learned from large datasets like ImageNet, so a pre-trained ResNet needs only a small labelled defect set for fine-tuning. This directly addresses the limited labelled images constraint, unlike training from scratch which would overfit.
About these practice questions
This AI0-001 question is part of Courseiva's 962-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.