- A
Hugging Face Transformers
Why wrong: Hugging Face Transformers is primarily for NLP models, not computer vision (though it has some vision models, it's not the best fit for general computer vision transfer learning).
- B
PyTorch
PyTorch provides torchvision with pre-trained models and torchvision.transforms for data augmentation, making it ideal for transfer learning in computer vision.
- C
scikit-learn
Why wrong: scikit-learn is for classical machine learning, not deep learning or computer vision transfer learning.
- D
TensorFlow
TensorFlow offers TensorFlow Hub and Keras Applications for pre-trained models, and tf.data for data augmentation, well-suited for computer vision.
- E
Keras
Why wrong: Keras is a high-level API that runs on top of TensorFlow; it is not a standalone framework for transfer learning.
AI0-001 AI Infrastructure and Technologies Practice Question
This AI0-001 practice question tests your understanding of ai infrastructure and technologies. 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 data scientist wants to develop a computer vision model using transfer learning. They need a framework that provides pre-trained models and easy-to-use APIs for data augmentation and training. Which TWO frameworks are best suited for this task?
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
PyTorch
PyTorch (option B) is correct because it offers a rich ecosystem of pre-trained models via `torchvision.models`, along with built-in data augmentation transforms in `torchvision.transforms` and a flexible training loop that is ideal for transfer learning. Its dynamic computation graph makes it easy to modify model architectures for fine-tuning, which is a core requirement for the task.
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.
- ✗
Hugging Face Transformers
Why it's wrong here
Hugging Face Transformers is primarily for NLP models, not computer vision (though it has some vision models, it's not the best fit for general computer vision transfer learning).
- ✓
PyTorch
Why this is correct
PyTorch provides torchvision with pre-trained models and torchvision.transforms for data augmentation, making it ideal for transfer learning in computer vision.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
scikit-learn
Why it's wrong here
scikit-learn is for classical machine learning, not deep learning or computer vision transfer learning.
- ✓
TensorFlow
Why this is correct
TensorFlow offers TensorFlow Hub and Keras Applications for pre-trained models, and tf.data for data augmentation, well-suited for computer vision.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Keras
Why it's wrong here
Keras is a high-level API that runs on top of TensorFlow; it is not a standalone framework for transfer learning.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between a high-level wrapper (Keras) and the underlying framework (TensorFlow) that actually provides the pre-trained models and training infrastructure, leading candidates to incorrectly select Keras as a standalone framework.
Detailed technical explanation
How to think about this question
Under the hood, PyTorch's `torchvision` package includes model architectures like ResNet, EfficientNet, and ViT with pre-trained weights from ImageNet, and its `transforms` module applies GPU-accelerated image augmentations (e.g., random crop, color jitter) directly on tensors. In a real-world scenario, a data scientist can freeze the feature extractor layers of a pre-trained ResNet-50 and replace the final fully connected layer to classify a custom dataset with limited images, leveraging PyTorch's automatic differentiation and optimizer APIs for efficient fine-tuning.
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 Infrastructure and Technologies — This question tests AI Infrastructure and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: PyTorch — PyTorch (option B) is correct because it offers a rich ecosystem of pre-trained models via `torchvision.models`, along with built-in data augmentation transforms in `torchvision.transforms` and a flexible training loop that is ideal for transfer learning. Its dynamic computation graph makes it easy to modify model architectures for fine-tuning, which is a core requirement for the task.
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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