How AI Works – The Basics Quiz – yesVirtual University

1. What is the foundation of AI learning?

A. Algorithms only
B. Data
C. Computers
D. Predictions

2. The more data an AI has, the better it can…

A. Memorize examples
B. Forget old patterns
C. Recognize patterns and make accurate predictions
D. Replace humans completely

3. Which is an example of AI learning from data?

A. Spam filter improving by analyzing thousands of labeled emails
B. A robot cooking without instructions
C. A smartphone charging faster
D. Google Maps showing directions

4. Structured data is usually stored in:

A. Pictures and videos
B. Rows and columns (like a spreadsheet)
C. Free text paragraphs
D. Social media posts

5. Which of these is an example of structured data?

A. Customer details in a database
B. A photo on Instagram
C. A song recording
D. A movie clip

6. Unstructured data includes:

A. Financial spreadsheets
B. Sales records
C. Emails, images, and videos
D. Employee ID numbers

7. Why is unstructured data important for modern AI?

A. Because it is easier to process
B. Because most of the world’s data is unstructured
C. Because it doesn’t need cleaning
D. Because it never contains bias

8. What happens if AI is trained with poor-quality data?

A. It performs better
B. It ignores the bad data
C. It makes inaccurate or biased predictions
D. It works the same as with good data

9. The phrase “garbage in, garbage out” means:

A. AI can recycle old data
B. Bad input data leads to bad output predictions
C. AI automatically cleans data
D. AI ignores biased data

10. In the cats vs. dogs example, what does the training stage involve?

A. Giving AI only unlabeled images
B. AI studying labeled images to learn patterns
C. Testing AI with unseen images
D. Releasing the AI app to users

11. What is an AI model?

A. The training dataset
B. A set of hardware components
C. The “brain” of AI that contains learned patterns
D. A robot that runs the AI system

12. During testing, AI is given:

A. Data it has already seen
B. Labeled data from training
C. New, unseen data
D. Only structured data

13. What problem occurs if an AI only memorizes training data?

A. Bias
B. Overfitting
C. Underfitting
D. Deployment error

14. In the “student exam” analogy, training is like:

A. Taking a final exam
B. Memorizing questions without understanding
C. Studying with solved examples
D. Forgetting lessons

15. In the AI Development Cycle, which step comes first?

A. Deployment
B. Evaluation
C. Data Collection
D. Training

16. What is the purpose of the evaluation step?

A. Deploying AI into real apps
B. Checking accuracy, performance, and fairness
C. Gathering more training data
D. Making predictions for customers

17. Deployment in AI means:

A. Storing the data
B. Teaching AI with labeled examples
C. Releasing the trained model for real-world use
D. Testing the AI again with training data

18. What makes AI continue improving after deployment?

A. Ignoring feedback
B. Using only structured data
C. Receiving new data and feedback
D. Shutting down unused models

19. Which of these is the correct order of the AI cycle?

A. Training → Data Collection → Deployment → Evaluation
B. Data Collection → Training → Evaluation → Deployment
C. Deployment → Evaluation → Data Collection → Training
D. Data Collection → Deployment → Training → Evaluation

20. What is the key takeaway of AI basics?

A. AI does not need data to work
B. AI is always unbiased
C. AI learns from large amounts of high-quality data to make smart decisions
D. AI never makes mistakes

How AI Works – The Basics Quiz

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