- A
Generative Adversarial Network (GAN)
Why wrong: Used for generating images, not classification.
- B
Convolutional Neural Network (CNN)
Specialized for image classification.
- C
Recurrent Neural Network (RNN)
Why wrong: Designed for sequences, not images.
- D
Transformer (e.g., Vision Transformer)
Can be applied to image classification with attention mechanisms.
- E
Multi-layer Perceptron (MLP)
Why wrong: Less effective for image classification due to lack of spatial hierarchy.
Question 918 of 1,000
AI0-001 AI Concepts and Techniques Practice Question
This AI0-001 practice question tests your understanding of ai concepts and techniques. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company wants to classify images of products into categories. They have a large dataset of labeled images. Which TWO types of neural networks are most suitable for this task? (Select TWO.)
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
✓
Convolutional Neural Network (CNN)
Convolutional Neural Networks (CNNs) are the standard architecture for image classification because they use convolutional layers to automatically learn spatial hierarchies of features (edges, textures, shapes) from pixel data. They are highly effective for large labeled image datasets due to their parameter efficiency and translation invariance.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Generative Adversarial Network (GAN)
Why it's wrong here
Used for generating images, not classification.
- ✓
Convolutional Neural Network (CNN)
Why this is correct
Specialized for image classification.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Recurrent Neural Network (RNN)
Why it's wrong here
Designed for sequences, not images.
- ✓
Transformer (e.g., Vision Transformer)
Why this is correct
Can be applied to image classification with attention mechanisms.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Multi-layer Perceptron (MLP)
Why it's wrong here
Less effective for image classification due to lack of spatial hierarchy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that any neural network can handle images, but the trap here is that RNNs and MLPs are technically capable of processing image data yet are fundamentally unsuitable for spatial feature extraction, leading candidates to select them over the correct specialized architectures.
Detailed technical explanation
How to think about this question
Vision Transformers (ViTs) apply the transformer architecture to image patches, using self-attention to capture global dependencies, which can outperform CNNs on large datasets. Under the hood, ViTs split an image into fixed-size patches, flatten them, and project them into embeddings, then process them through transformer encoder layers without convolutional inductive biases. In real-world scenarios, ViTs require more data and compute than CNNs but excel when pre-trained on massive datasets like ImageNet-21k.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Concepts and Techniques — This question tests AI Concepts and Techniques — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Convolutional Neural Network (CNN) — Convolutional Neural Networks (CNNs) are the standard architecture for image classification because they use convolutional layers to automatically learn spatial hierarchies of features (edges, textures, shapes) from pixel data. They are highly effective for large labeled image datasets due to their parameter efficiency and translation invariance.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
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.
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