Environmental impact of AI in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the total energy consumption of training an AI model.

Prompt Engineering / GenAI

total_energy = num_hours [1] power_usage_per_hour

Drag options to blanks, or click blank then click option'

A+

B-

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of multiplication

Dividing instead of multiplying

Energy consumption is calculated by multiplying the number of hours by the power usage per hour.

2fill in blank

medium

Complete the code to convert energy consumption from kilowatt-hours to megajoules.

Prompt Engineering / GenAI

energy_megajoules = energy_kwh [1] 3.6

Drag options to blanks, or click blank then click option'

A*

B/

C-

D+

Attempts:

3 left

💡 Hint

Common Mistakes

Dividing instead of multiplying

Adding the conversion factor

1 kilowatt-hour equals 3.6 megajoules, so multiply to convert.

3fill in blank

hard

Fix the error in the code to calculate carbon emissions from energy consumption.

Prompt Engineering / GenAI

carbon_emissions = energy_mj [1] emission_factor

Drag options to blanks, or click blank then click option'

A/

B+

C-

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition or division instead of multiplication

Carbon emissions are calculated by multiplying energy used by the emission factor.

4fill in blank

hard

Complete the code to create a dictionary of AI model names and their energy consumption in kWh.

Prompt Engineering / GenAI

energy_dict = {model: energy_kwh for model, energy_kwh [1] models_energy.items()}

Drag options to blanks, or click blank then click option'

A:

Bin

Con

D=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of ':' in dictionary

Using 'on' instead of 'in' for iteration

Use ':' to map keys to values in a dictionary, and 'in' to iterate over items.

5fill in blank

hard

Fill both blanks to filter AI models with energy consumption above 100 kWh and create a summary dictionary.

Prompt Engineering / GenAI

high_energy_models = {model: energy for model, energy [1] models_energy.items() if energy [2] 100}

Drag options to blanks, or click blank then click option'

A:

Bin

C>

D=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of ':' in dictionary

Using '<' instead of '>' for filtering

Use ':' to map keys to values, 'in' to iterate, and '>' to filter values above 100.

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