- A
Use FP32 precision instead of mixed precision
Why wrong: FP32 requires more memory and compute than mixed precision (FP16), increasing energy use.
- B
Apply model pruning and knowledge distillation
Pruning removes unnecessary weights and distillation trains a smaller student model, both reducing the computational load and energy footprint.
- C
Use more GPUs in parallel to finish training faster
Why wrong: More GPUs increase total energy consumption even if wall-clock time decreases, unless the workload perfectly scales.
- D
Increase the number of training epochs for better accuracy
Why wrong: More epochs mean more computation and thus higher energy consumption.
Question 290 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 data scientist is training a large language model and wants to reduce the carbon footprint. Which practice is MOST effective for reducing energy consumption during training?
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 pruning and knowledge distillation
Model pruning reduces the number of parameters in the model, and knowledge distillation trains a smaller student model to mimic a larger teacher model. Both techniques directly reduce the computational operations (FLOPs) required during training and inference, leading to significant energy savings. In contrast, using FP32 or increasing epochs increases energy consumption, and adding more GPUs increases total power draw even if wall-clock time decreases.
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.
- ✗
Use FP32 precision instead of mixed precision
Why it's wrong here
FP32 requires more memory and compute than mixed precision (FP16), increasing energy use.
- ✓
Apply model pruning and knowledge distillation
Why this is correct
Pruning removes unnecessary weights and distillation trains a smaller student model, both reducing the computational load and energy footprint.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use more GPUs in parallel to finish training faster
Why it's wrong here
More GPUs increase total energy consumption even if wall-clock time decreases, unless the workload perfectly scales.
- ✗
Increase the number of training epochs for better accuracy
Why it's wrong here
More epochs mean more computation and thus higher energy consumption.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that 'finishing faster always saves energy,' but the trap here is that using more GPUs increases total power draw, and the energy equation (power × time) often results in higher overall consumption due to parallelization overhead and idle power.
Detailed technical explanation
How to think about this question
Knowledge distillation leverages a temperature-scaled softmax to transfer dark knowledge from a teacher to a student model, often achieving 90%+ of the teacher's accuracy with 10-50% of the parameters. Pruning can be structured (removing entire neurons or channels) or unstructured (zeroing individual weights), with structured pruning being more hardware-friendly for GPU acceleration. In practice, combining pruning and distillation can reduce training energy by 40-70% while maintaining acceptable performance, as seen in models like DistilBERT.
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.
TExam Day Tips
- 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Apply model pruning and knowledge distillation — Model pruning reduces the number of parameters in the model, and knowledge distillation trains a smaller student model to mimic a larger teacher model. Both techniques directly reduce the computational operations (FLOPs) required during training and inference, leading to significant energy savings. In contrast, using FP32 or increasing epochs increases energy consumption, and adding more GPUs increases total power draw even if wall-clock time decreases.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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