AI0-001 AI Infrastructure and Technologies Practice Question
A data science team is deploying a real-time fraud detection model on edge devices in retail stores. The model must infer under 10ms and fit within 50MB memory. Which combination of techniques should the team apply?
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
✓
Model quantization to INT8 and pruning of low-weight connections
Quantization reduces model precision (e.g., FP32 to INT8) to shrink memory and speed up inference, while pruning removes redundant parameters. Distillation can further compress. These are standard for edge deployment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Model parallelism and distributed inference
Why it's wrong here
Model parallelism is for multi-device setups; edge devices are typically single-device, adding complexity without meeting constraints.
- ✗
Increase batch size and use FP16 precision
Why it's wrong here
Increasing batch size is not suitable for real-time single inference; FP16 may not reduce memory enough for 50MB limit.
- ✗
Train a larger model and use distillation to transfer knowledge
Why it's wrong here
Training a larger model first and then distilling is a valid approach, but the question asks for techniques applied to the deployed model; quantization and pruning are more direct.
- ✓
Model quantization to INT8 and pruning of low-weight connections
Why this is correct
INT8 quantization reduces model size and latency; pruning eliminates unnecessary weights, meeting both memory and speed constraints.
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.