Environmental impact of AI in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding AI Energy Consumption

Which factor most directly increases the energy consumption of training a large AI model?

A Increasing the number of training data samples

B Using a simpler model architecture

C Reducing the number of training epochs

D Switching from GPU to CPU for training

💡 Hint

Think about what makes the computer work harder during training.

More training data means the model processes more information, which takes more energy. Simpler models or fewer epochs reduce energy use. CPUs are usually less efficient than GPUs for AI training, so switching to CPU often increases energy use.

❓ Metrics

intermediate

Measuring Carbon Footprint of AI Training

If training a model uses 500 kWh of electricity and the local grid emits 0.4 kg CO2 per kWh, what is the total CO2 emission from training?

A 0.8 kg CO2

B 1250 kg CO2

C 200 kg CO2

D 500 kg CO2

💡 Hint

Multiply energy used by emission per unit energy.

Total CO2 = Energy (kWh) × Emission factor (kg CO2/kWh) = 500 × 0.4 = 200 kg CO2.

❓ Model Choice

advanced

Choosing Models to Reduce Environmental Impact

Which model choice best reduces environmental impact while maintaining reasonable accuracy?

A A very large transformer model trained from scratch on massive data

B Using a large model with many training epochs without early stopping

C Training multiple large models and averaging their predictions

D A smaller pre-trained model fine-tuned on specific data

💡 Hint

Fine-tuning smaller models uses less energy than training large models from zero.

Fine-tuning a smaller pre-trained model requires less energy and time than training a large model from scratch, reducing environmental impact while keeping good accuracy.

🔧 Debug

advanced

Identifying Energy Waste in AI Training Code

What is the main issue causing unnecessary energy use in this training loop? ```python for epoch in range(100): train(model, data) if epoch % 10 == 0: evaluate(model, val_data) # No early stopping or checkpoint saving ```

A Lack of early stopping causes training to continue even if performance stops improving

B The data is not shuffled before training, causing poor model performance

C The training loop uses too few epochs, causing inefficient learning

D The model is evaluated too frequently, wasting energy

💡 Hint

Stopping training early when the model stops improving saves energy.

Without early stopping, the model trains for all 100 epochs even if it stops improving early, wasting energy.

🧠 Conceptual

expert

Evaluating Environmental Impact Trade-offs

Which strategy best balances AI model performance and environmental impact for a company deploying AI services globally?

A Train multiple large models for each region to maximize accuracy locally

B Use smaller, optimized models deployed closer to users with edge computing

C Train large models centrally and serve predictions globally without optimization

D Continuously retrain large models daily regardless of performance changes

💡 Hint

Deploying smaller models near users reduces data transfer and energy use.

Deploying optimized smaller models near users (edge computing) reduces energy from data transfer and large centralized servers, balancing performance and environmental impact.

Practice

1. What is the main environmental concern related to training large AI models?

easy

A. AI models increasing water pollution

B. AI models causing deforestation directly

C. AI models producing plastic waste

D. High energy consumption leading to increased carbon emissions

Solution

1. Step 1: Understand AI training process

   Training large AI models requires a lot of computer power, which uses electricity.

2. Step 2: Link electricity use to environmental impact

   Electricity often comes from burning fossil fuels, which releases carbon emissions harming the environment.

3. Final Answer:

   High energy consumption leading to increased carbon emissions -> Option D

4. Quick Check:

   Energy use = Carbon emissions [OK]

Hint: Think about what powers computers during training [OK]

Common Mistakes:

• Confusing AI's indirect impact with direct pollution
• Thinking AI models produce physical waste
• Ignoring energy source in environmental impact

2. Which of the following is the correct way to reduce AI's environmental impact?

easy

A. Use larger models with more layers

B. Train models using renewable energy sources

C. Increase training time without optimization

D. Ignore energy consumption during model design

Solution

1. Step 1: Identify methods to reduce carbon footprint

   Using renewable energy like solar or wind reduces carbon emissions from electricity.

2. Step 2: Evaluate options for environmental friendliness

   Options A, B, and D increase energy use or ignore it, so they don't reduce impact.

3. Final Answer:

   Train models using renewable energy sources -> Option B

4. Quick Check:

   Renewable energy = Lower carbon footprint [OK]

Hint: Choose options that lower energy or use clean energy [OK]

Common Mistakes:

• Thinking bigger models always help
• Ignoring energy source in training
• Assuming longer training is better for environment

3. Consider this code snippet estimating AI model energy use:

energy_per_epoch = 50  # kWh
epochs = 10
carbon_per_kwh = 0.4  # kg CO2
carbon_footprint = energy_per_epoch * epochs * carbon_per_kwh
print(carbon_footprint)

What is the output of this code?

medium

A. 200.0

B. 500.0

C. 20.0

D. 400.0

Solution

1. Step 1: Calculate total energy used

   Energy per epoch (50 kWh) times epochs (10) equals 500 kWh total.

2. Step 2: Calculate carbon footprint

   Multiply total energy (500 kWh) by carbon per kWh (0.4 kg CO2) = 200 kg CO2.

3. Final Answer:

   200.0 -> Option A

4. Quick Check:

   50 * 10 * 0.4 = 200.0 [OK]

Hint: Multiply energy, epochs, and carbon per kWh [OK]

Common Mistakes:

• Multiplying incorrectly or missing one factor
• Confusing units or decimal points
• Mixing up variable names

4. This code tries to calculate carbon footprint but has a bug:

energy_per_epoch = 40
epochs = '10'
carbon_per_kwh = 0.3
carbon_footprint = energy_per_epoch * epochs * carbon_per_kwh
print(carbon_footprint)

What is the error and how to fix it?

medium

A. SyntaxError due to missing colon

B. NameError because carbon_per_kwh is undefined

C. TypeError because epochs is a string; convert it to int

D. No error; code runs fine

Solution

1. Step 1: Identify variable types

   epochs is a string '10', but multiplication needs a number.

2. Step 2: Fix type mismatch

   Convert epochs to integer using int(epochs) to allow multiplication.

3. Final Answer:

   TypeError because epochs is a string; convert it to int -> Option C

4. Quick Check:

   String * float causes error [OK]

Hint: Check variable types before math operations [OK]

Common Mistakes:

• Ignoring type mismatch errors
• Assuming code runs without conversion
• Confusing error types

5. You want to reduce the environmental impact of an AI project. Which combined approach is best?

hard

A. Use smaller models, train fewer epochs, and power training with renewable energy

B. Use larger models, train longer, and use coal-based electricity

C. Ignore model size, focus only on data quality

D. Train models on any energy source but optimize only accuracy

Solution

1. Step 1: Identify factors affecting environmental impact

   Model size, training time, and energy source all affect energy use and emissions.

2. Step 2: Combine best practices

   Smaller models and fewer epochs reduce energy use; renewable energy lowers carbon footprint.

3. Final Answer:

   Use smaller models, train fewer epochs, and power training with renewable energy -> Option A

4. Quick Check:

   Smaller + less training + clean energy = less impact [OK]

Hint: Combine smaller models, less training, and clean energy [OK]

Common Mistakes:

• Focusing on accuracy only
• Ignoring energy source
• Assuming bigger models are better for environment