AI0-001 AI Infrastructure and Technologies Practice Question
A company is deploying a real-time object detection model on a fleet of IoT cameras. The model must run at 30 FPS on a device with limited memory and no internet connectivity. Which combination of techniques is MOST suitable?
⚠ Common exam trap
CompTIA often tests the misconception that any lightweight deployment framework (like ONNX Runtime) is sufficient for edge devices, ignoring the need for hardware-specific quantization and pruning to meet strict memory and FPS constraints.
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
✓
Apply INT8 quantization and pruning, then deploy using TensorFlow Lite
INT8 quantization reduces model size and latency, while pruning removes redundant weights, making the model suitable for memory-constrained edge devices. TensorFlow Lite is optimized for on-device inference with no internet dependency, supporting real-time 30 FPS object detection on IoT cameras.
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 FP16 inference and deploy via Docker containers
Why it's wrong here
FP16 reduces precision but not as aggressively as INT8; Docker adds overhead and requires an OS, which may be too heavy for IoT devices.
- ✗
Use model distillation to create a smaller model and deploy via ONNX Runtime
Why it's wrong here
Distillation can create a smaller model, but ONNX Runtime may not be as lightweight as TensorFlow Lite for specific IoT hardware.
- ✗
Deploy on a GPU-based edge server with a full PyTorch model
Why it's wrong here
GPUs may not be available on low-power IoT cameras; the full model is too large and consumes too much power.
- ✓
Apply INT8 quantization and pruning, then deploy using TensorFlow Lite
Why this is correct
INT8 quantization reduces memory footprint and accelerates inference; pruning removes redundant parameters. TensorFlow Lite is optimized for edge devices.
About these practice questions
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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.