AI0-001 AI Governance and Ethics Practice Question
A company wants to reduce the carbon footprint of training large AI models. Which practice is MOST effective for achieving 'Green AI'?
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
✓
Prune the model to reduce its size before training
Green AI practices focus on reducing computational and environmental costs. Using model pruning reduces model size and computational requirements. Using more GPUs increases energy consumption. Training with larger datasets increases compute. Using older hardware is often less energy-efficient.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Train on larger datasets to improve accuracy
Why it's wrong here
Larger datasets require more compute and energy, increasing the carbon footprint.
- ✓
Prune the model to reduce its size before training
Why this is correct
Pruning reduces the number of parameters and computations, directly lowering energy consumption.
- ✗
Use more powerful GPUs to speed up training
Why it's wrong here
More powerful GPUs may speed up training but often consume more energy, increasing the carbon footprint.
- ✗
Use older, less efficient hardware to save on manufacturing emissions
Why it's wrong here
Older hardware is typically less energy-efficient, leading to higher operational emissions.
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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.