AI0-001 AI Governance and Ethics Practice Question
A company is training a large language model and wants to reduce its carbon footprint. Which practice is MOST effective for reducing training energy consumption while maintaining model quality?
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
✓
Use mixed-precision training and prune unnecessary parameters
Green AI practices include using more efficient hardware (like GPUs with lower power draw), model pruning, and early stopping. Using CPUs is slower and less efficient. Increasing batch size without tuning can hurt convergence. Using a larger model increases energy. Training on renewable energy reduces the carbon impact but does not reduce energy consumption itself.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the batch size to the maximum the GPU memory allows
Why it's wrong here
Very large batch sizes can negatively impact convergence and may require more epochs, potentially increasing total energy.
- ✗
Use a larger model architecture to achieve higher accuracy faster
Why it's wrong here
Larger models require more computation and energy; they do not reduce energy consumption.
- ✓
Use mixed-precision training and prune unnecessary parameters
Why this is correct
Mixed-precision training reduces compute and memory usage, and pruning reduces model size, both lowering energy consumption.
- ✗
Train the model on CPUs instead of GPUs
Why it's wrong here
CPUs are less energy-efficient for deep learning training than GPUs, typically consuming more energy per training step.
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.