AI0-001 AI Governance and Ethics Practice Question
A company wants to adopt green AI practices to reduce the environmental impact of training large models. Which TWO actions are most effective?
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 efficient model architectures (e.g., pruning, quantization)
Using efficient model architectures (A) and energy-efficient hardware (E) directly reduce energy consumption. Using larger datasets (B) increases energy use. Training models on weekends (C) does not affect total energy consumption. Training models in the cloud (D) may offload energy costs to the provider but does not reduce total energy used.
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 efficient model architectures (e.g., pruning, quantization)
Why this is correct
Pruning and quantization shrink parameter counts and numeric precision, cutting the compute and energy consumed during training and inference. This directly satisfies the stem's goal of reducing environmental impact, since training cost scales with model size and floating-point operations.
- ✗
Use larger datasets to improve accuracy
Why it's wrong here
Enlarging datasets increases training compute, storage and energy consumption, directly worsening the environmental footprint the scenario seeks to reduce. It is tempting because larger corpora typically raise accuracy and generalisation, which is the correct choice when the requirement is model quality rather than green AI.
- ✗
Train models only on weekends
Why it's wrong here
Scheduling training on weekends changes only when compute runs, not how much energy is consumed or its carbon intensity; total GPU hours and grid mix remain unchanged. It is tempting because time-shifting to off-peak periods can lower cost and occasionally align with cleaner generation, which is the correct tactic when the objective is demand response or tariff optimisation.
- ✗
Train models in the cloud to offload energy costs
Why it's wrong here
Cloud migration relocates energy consumption rather than reducing it, and provider regions vary in carbon intensity, so no emissions reduction is guaranteed. It is tempting because cloud providers offer efficiency at scale and renewable-energy purchasing, which is the right answer when the goal is cost or capacity elasticity rather than measured carbon reduction.
- ✓
Use energy-efficient hardware (e.g., TPUs or optimized GPUs)
Why this is correct
Energy-efficient accelerators such as TPUs or optimised GPUs deliver more computations per watt, directly lowering the electricity consumed during large-model training. This addresses the stem's constraint of reducing the environmental impact of training, making it one of the most effective green AI actions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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