Final Book Exam

Total 50 marks. Show your working. Structure: A. Fill blanks (10), B. Compute (10), C. Code reading (10), D. Short answers (10), E. Long answer (10).

A. Fill in the blanks (10 marks)

1. The ‘spread’ of data is measured by its deviation.
2. Two vectors are most similar when their product is largest.
3. All probabilities of one event always sum to .
4. An O(n²) algorithm with n = 1000 takes steps.
5. The Python library for classic ML in this book is -.
6. The standard train/test split ratio is / .
7. India’s open-data portal address is .
8. The ‘Hello World’ dataset of ML is the dataset.
9. df.dropna() in pandas does what to missing values: .
10. Aanvi’s hackathon project achieved % accuracy.

B. Compute (10 marks)

1. Mean of [10, 20, 30, 40, 50] =
2. Dot product of [1, 2, 3] and [4, 5, 6] =
3. P(at least 1 head in 2 coin flips) =
4. (True AND False) OR (NOT False) =
5. If P(spam) = 0.95, P(not spam) =
6. Big-O of looping through every PAIR of items = O()
7. Median of [3, 7, 2, 8, 9, 1, 5] =
8. If a model gets 47 of 50 test predictions right, accuracy = %
9. (5 > 3) AND (4 == 4) =
10. Standard deviation of [5, 5, 5, 5, 5] =

C. Read & predict the output (10 marks)

1. nums = [3, 1, 4, 1, 5]; print(sum(nums)/len(nums)) →
2. print(15 // 4, 15 % 4) →
3. for i in range(3): print(i*i) →
4. from sklearn.datasets import load_iris; print(len(load_iris().data)) →
5. import random; random.seed(0); print(random.randint(1,6)) →

D. Short answers (10 marks)

1. Explain in your own words: why probabilities must sum to 1.
2. Why is the dot product useful for AI? Give one concrete example.
3. Compare Big-O O(n) vs O(n²). When does the difference matter?
4. Write 3 lines of Python that compute the mean of a list.
5. What is the 80/20 train-test split and why do we need it?

E. Long answer — pick ANY ONE (10 marks)

1. Describe an AI project YOU would build in the next 6 months. Cover: the problem in one sentence, what data you need, what model, how you’d evaluate it, and one ethical concern.
2. Pick TWO teen builders from this book. For each, write what you found most inspiring and one specific thing you would do differently.
3. Argue: ‘Maths matters more than coding for AI.’ Use at least 3 examples from this book. Then argue the opposite. Which side do YOU believe more strongly, and why?