AI Literacy Quiz - IMD business school for management and leadership courses

Welcome to your AI Literacy Quiz

AI’s ability to process and generate language, images, audio, and other things, combined with its capacity to learn and adapt, positions it as a transformative agent that will reshape nearly every aspect of our lives.

That's why AI literacy is a foundational skill to have for the 21st century to boost productivity, creativity and efficiency, but also to ensure a future where technology advances global prosperity.

Take this short quiz and test your AI Literacy skills.

1. What is the primary difference between traditional software and AI-powered systems?

AI-powered systems are designed to learn and improve over time.

Traditional software can make decisions without human intervention.

AI systems do not require any data to function.

Traditional software always uses neural networks.

2. Which of the following best describes machine learning?

A set of rules programmed explicitly by humans.

A system that improves performance by learning from data.

A hardware-based AI solution.

A type of AI that does not require human supervision.

3. What is an example of supervised learning in AI?

An AI system that generates text without prior training data.

A chatbot that learns through trial and error.

A system that detects spam emails based on labeled data.

A robot that explores an environment with no guidance.

4. How can AI create business value?

By ensuring 100% accurate predictions.

By replacing all human decision-making processes.

By eliminating the need for cybersecurity measures.

By automating repetitive tasks to improve efficiency.

5. Which of the following AI applications would be most beneficial for a supply chain leader?

AI-generated social media content.

Predictive analytics for demand forecasting.

AI-powered legal document review.

AI-driven medical diagnosis.

6. In AI-driven decision-making, what is the biggest risk executives need to manage?

Over-reliance on AI without human oversight.

The inability of AI to process large amounts of data.

The increased cost of using AI compared to manual processes.

AI’s ability to operate without requiring any updates.

7. What is a common ethical concern when implementing AI in business?

AI always provides unbiased decisions.

AI systems require frequent hardware upgrades.

AI can reinforce existing biases in data.

AI can replace employees without any resistance.

8. What is "explainability" in AI, and why is it important?

It refers to how well an AI system can function without human supervision.

It refers to AI systems being able to explain their reasoning to other AI systems.

It refers to making AI decisions understandable to humans, ensuring trust and accountability.

It is a measure of AI’s ability to replace human intuition.

9. What is a major limitation of AI models like ChatGPT?

They can never generate human-like responses.

They do not require training on large datasets.

They may generate factually incorrect or biased outputs.

They can understand human emotions perfectly.

10. How does AI in cybersecurity improve organizational resilience?

By completely eliminating cyber threats.

By detecting and responding to threats faster than humans can.

By replacing the need for human cybersecurity teams.

By preventing employees from using personal devices.

11. How should business executives prepare their workforce for AI adoption?

By upskilling employees to work alongside AI.

By replacing employees with AI wherever possible.

By eliminating human oversight in AI-driven decision-making.

By banning AI from the organization.

12. What is the role of data in AI effectiveness?

AI models do not need continuous updates once deployed.

AI models do not require large datasets to make good decisions.

AI models work equally well with any kind of data, regardless of quality.

AI models perform best when trained on high-quality, diverse, and relevant data.

13. What should an executive consider before deploying AI in their organization?

The quality and availability of data.

The ethical implications of AI deployment.

The costs and return on investment.

All of the above.

14. Which of these statements is FALSE?

AI-based systems cannot generate content that is genuinely new.

AI-based systems can interpret language and infer the feelings of humans from that language.

AI-based systems can use synthetic data to improve their performance.

AI-based systems cannot reason like humans.

15. A company is deploying an AI-driven customer service chatbot. During testing, executives notice that the chatbot responds differently to customer complaints based on demographic factors. What is the best course of action?

Continue deployment since the chatbot has passed standard accuracy benchmarks.

Fine-tune the AI by removing demographic data to eliminate bias.

Audit the training data and model outputs to identify and mitigate bias sources before deployment.

Switch to a different AI provider.

16. A CEO wants to integrate AI into financial forecasting. The AI vendor guarantees 95% accuracy in predictions, but the executives remain cautious. What is the most critical question they should ask?

Does the model perform better than our current human-led forecasting?

What is the explainability of the AI’s predictions, and how does it handle uncertainty?

How much money will we save by automating financial forecasting?

Can we scale this AI solution to all areas of our business?

17. A multinational corporation is considering a generative AI tool for internal knowledge management. What is the biggest risk the executive team must address before deployment?

Ensuring that employees can use the tool effectively.

The risk of AI-generated misinformation or hallucinations in critical business insights.

Determining the cost savings compared to traditional knowledge management.

Integrating the AI with existing databases and document repositories.

18. How does the EU AI Act classify AI systems based on risk?

Low risk, medium risk, high risk, critical risk

Basic risk, advanced risk, expert risk, extreme risk

Prohibited risk, regulated risk, supervised risk, unsupervised risk

Unacceptable risk, high risk, limited risk, minimal risk

19. What is a key consideration when using AI to process customer data under GDPR?

Ensuring all data is publicly available.

Using advanced models regardless of data sensitivity.

Obtaining consent and anonymizing data where possible.

Storing data indefinitely for future training.

20. What is a primary challenge in maintaining an AI model post-deployment?

The model becomes 100% accurate over time.

Employees resist using the AI model.

AI models do not require maintenance once deployed.

Data drift and changing real-world conditions require updates.

21. Which of these types of learning is based on how humans learn – a system trial and error and incentives to encourage ‘good’ actions?

Reinforcement Learning

Symbolic Learning

Humanoid Learning

Limitless Learning

22. Which of the following can give you the greatest source of competitive advantage from Generative AI?

Your computing power

Your data

Specialized algorithms that only you have access to

None of the above

23. Which of the following is currently an issue with Generative AI technology?

Cost per query is quite high

The carbon emissions from training and using GenAI

Water consumption from cooling requirements

All of the above