
AI and Climate Change Quiz

Interactive Video
•
Science, Computers, Business
•
10th Grade - University
•
Hard

Mia Campbell
FREE Resource
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10 questions
Show all answers
1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why are instructions that only tell you what to do often considered ineffective?
They are too detailed.
They are written in a complex language.
They lack the 'how' to implement the steps.
They are too simple.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What are the three critical roles of AI in addressing climate change?
Replacing fossil fuels, reducing emissions, and increasing efficiency.
Understanding climate change, optimizing systems, and accelerating science.
Monitoring weather patterns, predicting disasters, and managing resources.
Developing new technologies, creating policies, and educating the public.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a major challenge of using renewable energy sources like wind?
They are too expensive to implement.
They are unpredictable and cannot be reliably scheduled.
They require too much land.
They produce too much energy.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What was the primary goal of the AI system developed by the team?
To reduce the cost of wind energy production.
To replace fossil fuels entirely.
To predict wind energy availability more accurately.
To create new wind turbines.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What was a significant outcome of the AI system's deployment with Google?
It was 20% more accurate than previous systems.
It reduced the cost of wind energy by 50%.
It eliminated the need for fossil fuels.
It doubled the energy output of wind farms.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a key factor in successfully deploying AI systems in real-world scenarios?
Using only open-source data.
Focusing solely on theoretical research.
Finding partners with domain expertise and willingness to test.
Having unlimited funding.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a major hurdle in deploying AI in the real world?
Difficulty in finding willing deployment partners.
Lack of interest from researchers.
Insufficient computing power.
Too much available data.
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