- A
Increase the batch size to the maximum the GPU memory allows
Why wrong: Very large batch sizes can negatively impact convergence and may require more epochs, potentially increasing total energy.
- B
Use a larger model architecture to achieve higher accuracy faster
Why wrong: Larger models require more computation and energy; they do not reduce energy consumption.
- C
Use mixed-precision training and prune unnecessary parameters
Mixed-precision training reduces compute and memory usage, and pruning reduces model size, both lowering energy consumption.
- D
Train the model on CPUs instead of GPUs
Why wrong: CPUs are less energy-efficient for deep learning training than GPUs, typically consuming more energy per training step.
Question 9 of 1,000
AI0-001 AI Governance and Ethics Practice Question
This AI0-001 practice question tests your understanding of ai governance and ethics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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AI Governance and Ethics — study guide chapter
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Governance and Ethics — This question tests AI Governance and Ethics — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI0-001 question wrong?
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 2026
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.
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