AI0-001 AI Governance and Ethics Practice Question
A research lab is training a large language model and wants to minimize its environmental impact. Which THREE practices are most effective for reducing the carbon footprint of model training?
⚠ Common exam trap
A common pitfall is assuming that more training epochs or larger models always lead to better performance. However, extending epochs or increasing model size significantly raises energy consumption and carbon emissions, contradicting the goal of minimizing environmental impact.
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 model compression techniques like pruning and quantization
Model compression techniques like pruning and quantization directly reduce the computational requirements of training and inference. Pruning removes redundant weights, and quantization reduces the precision of weights (e.g., from 32-bit to 8-bit), which lowers the number of operations and memory bandwidth needed, thereby decreasing energy consumption and carbon emissions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Apply model compression techniques like pruning and quantization
Why this is correct
Compression reduces model size and inference cost, and can also reduce training energy.
- ✗
Extend the number of training epochs to ensure convergence
Why it's wrong here
More epochs increase total energy consumption.
- ✓
Train the model on a data center powered by renewable energy
Why this is correct
Using renewable energy reduces the carbon footprint even if energy consumption is unchanged.
- ✓
Use energy-efficient hardware such as TPUs or low-power GPUs
Why this is correct
Efficient hardware reduces energy consumption per computation.
- ✗
Increase the model size to achieve better accuracy faster
Why it's wrong here
Larger models require more computation and energy, not less.
About these practice questions
This AI0-001 question is part of Courseiva's 962-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.