ASSESSMENT 1

Q1. Which of the following is NOT a type of algorithm design paradigm?

• • A) Divide and Conquer

• • B) Greedy

• • C) Dynamic Programming

• • D) Static Programming

Q2. In the context of sorting algorithms, what is the average-case time complexity of Quick Sort?

• • A) O(n)

• • B) O(n log n)

• • C) O(n^2)

• • D) O(log n)

Q3. What is the worst-case time complexity of Bubble Sort?

• • A) O(n)

• • B) O(n log n)

• • C) O(n^2)

• • D) O(log n)

Q4. Which algorithm is efficient for large datasets and has a time complexity of O(n log n)?

• • A) Insertion Sort

• • B) Merge Sort

• • C) Selection Sort

• • D) Bubble Sort

Q5. In the context of algorithms, what does 'Big O notation' describe?

• • A) The average-case time complexity of an algorithm

• • B) The space complexity of an algorithm

• • C) The upper bound of an algorithm's time complexity

• • D) The lower bound of an algorithm's space complexity

Q6. What is the time complexity of the following code?

for (int i = 0; i < n; i++) {

for (int j = 0; j < i; j++) {

// Constant time operation

}

}

• • A) O(n)

• • B) O(n log n)

• • C) O(n^2)

• • D) O(n^3)

Q7. Find the time complexity of the following code:

for (int i = 0; i < n; i++) {

for (int j = 0; j < n; j++) {

for (int k = 0; k < n; k++) {

// Constant time operation

}

}

}

• • A) O(n)

• • B) O(n²)

• • C) O(n³)

• • D) O(2ⁿ)

Q8. What is the time complexity of the following code?

for (int i = 1; i <= n; i *= 2) {

for (int j = 0; j < n; j++) {

// Constant time operation

}

}

• • A) O(n)

• • B) O(n log n)

• • C) O(n²)

• • D) O(log n)

Q9. Which notation describes the lower bound of an algorithm's time complexity?

• • A) O-notation

• • B) Θ-notation

• • C) Ω-notation

• • D) o-notation

Q10. If an algorithm has a time complexity of O(n²), which of the following statements is true?

• • A) The algorithm's running time grows faster than n^2 for large n.

• • B) The algorithm's running time grows at most as fast as n^2 for large n.

• • C) The algorithm's running time is exactly n^2 for all n.

• • D) The algorithm's running time grows slower than n^2 for large n.