AI0-001 AI Infrastructure and Technologies Practice Question
A developer is building a mobile app that uses a pre-trained image classification model on-device. Which framework should they use to run the model on iOS devices?
⚠ Common exam trap
AI0-001 often tests the choice of framework for specific platforms, and candidates may choose cross-platform tools like TensorFlow Lite when a native solution like Core ML is more appropriate.
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
✓
Core ML
Core ML is the correct framework because it is Apple's machine learning framework designed specifically for on-device inference on iOS, macOS, watchOS, and tvOS. It provides optimized performance and integration with Apple's hardware, making it the best choice for running a pre-trained image classification model on iOS devices.
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 a Python library for loading and fine-tuning models, not an on-device iOS runtime. It is tempting because it hosts the pre-trained image classification models themselves, and would be the right choice for server-side inference or model training, but it cannot execute a model on an iPhone.
- ✗
TensorFlow Lite
Why it's wrong here
TensorFlow Lite runs on iOS through its interpreter, but Core ML is Apple's native framework, giving direct access to the Neural Engine and GPU. TensorFlow Lite fits cross-platform apps sharing one model across Android and iOS, not iOS-only deployments.
- ✗
PyTorch Mobile
Why it's wrong here
PyTorch Mobile targets Android and iOS via TorchScript, but Apple's Core ML is the native on-device runtime for iOS, offering optimised execution and hardware acceleration. PyTorch Mobile suits cross-platform deployments where a single model serves both Android and iOS.
- ✓
Core ML
Why this is correct
Core ML is Apple's native on-device inference framework, so it runs the pre-trained image classification model directly on iOS hardware without a network round trip. It satisfies the stem's on-device constraint, unlike cloud-hosted alternatives, and integrates with Xcode tooling to convert and optimise models for Apple silicon.
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 →
JA
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.