Prepare for Time Complexity of Algorithms Interview with iQwizz | Difficulty Level: Intermediate | Rating: 4.6

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Question 1

What is the time complexity of searching for an element in an unsorted array of size 'n' in the worst-case scenario?

O(log n)
O(1)
O(n)
O(n^2)

Question 2

An algorithm has a time complexity of O(n log n). Which of the following statements is NOT necessarily true?

The algorithm's best-case runtime could be O(log n).
The algorithm's runtime grows at most as fast as n log n.
The algorithm will always be slower than an algorithm with O(n) complexity.
For large enough input sizes, the runtime is bounded above by some constant multiple of n log n.

Question 3

The Master Theorem is used to solve recurrence relations of a specific form. Which of the following forms is NOT suitable for the Master Theorem?

T(n) = √n * T(√n) + n
All of the above are suitable for the Master Theorem
T(n) = aT(n/b) + f(n)
T(n) = aT(n-b) + f(n)

Question 4

What is the time complexity of inserting an element at the beginning of a singly linked list?

O(log n)
O(1)
O(n log n)
O(n)

Question 5

What does it mean for an algorithm to have a time complexity of Θ(1)?

The algorithm's runtime is unpredictable and varies greatly with different inputs.
The runtime grows linearly with the input size.
The runtime is constant and independent of the input size.
The algorithm is guaranteed to be the fastest possible solution for the problem.

Question 6

An algorithm processes an input of size 'n' by dividing it in half repeatedly until the size becomes 1. What is the most likely time complexity of this algorithm?

O(log n)
O(n^2)
O(n log n)
O(n)

Question 7

You need to choose between two algorithms for your application. Algorithm A has a time complexity of O(n log n) but is known to be difficult to implement efficiently. Algorithm B has a time complexity of O(n^2) but is straightforward to implement and likely to have lower constant factors. Which of the following factors is LEAST important in deciding which algorithm to choose?

The programming language you are using.
The development time and resources available.
The size of the datasets your application will handle.
The acceptable performance limits of your application.

Question 8

You have optimized an algorithm to improve its time complexity from O(n^2) to O(n log n). When you benchmark the optimized code on real-world data, you find that it's only slightly faster for small inputs and doesn't show significant improvement for larger datasets. What's the most likely explanation?

The O(n log n) algorithm has a large constant factor hidden by Big O notation.
The input data is not random, and the optimized algorithm doesn't handle the specific data patterns well.
You made a mistake in your code optimization.
The benchmarking process is flawed.

Question 9

If an algorithm has a time complexity of Ω(n log n), what can you conclude about its best-case runtime?

It will always be slower than an algorithm with O(n^2) time complexity.
It cannot be faster than an algorithm with O(log n) time complexity.
It will have a constant runtime regardless of input size.
It will always be faster than an algorithm with O(n) time complexity.

Question 10

A linear search algorithm iterates through an unsorted array to find a target element. What is its average-case time complexity?

O(n²)
O(1)
O(n)
O(log n)

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Question 1

What is the time complexity of searching for an element in an unsorted array of size 'n' in the worst-case scenario?

O(log n)
O(1)
O(n)
O(n^2)

Question 2

An algorithm has a time complexity of O(n log n). Which of the following statements is NOT necessarily true?

The algorithm's best-case runtime could be O(log n).
The algorithm's runtime grows at most as fast as n log n.
The algorithm will always be slower than an algorithm with O(n) complexity.
For large enough input sizes, the runtime is bounded above by some constant multiple of n log n.

Question 3

The Master Theorem is used to solve recurrence relations of a specific form. Which of the following forms is NOT suitable for the Master Theorem?

T(n) = √n * T(√n) + n
All of the above are suitable for the Master Theorem
T(n) = aT(n/b) + f(n)
T(n) = aT(n-b) + f(n)

Question 4

What is the time complexity of inserting an element at the beginning of a singly linked list?

O(log n)
O(1)
O(n log n)
O(n)

Question 5

What does it mean for an algorithm to have a time complexity of Θ(1)?

The algorithm's runtime is unpredictable and varies greatly with different inputs.
The runtime grows linearly with the input size.
The runtime is constant and independent of the input size.
The algorithm is guaranteed to be the fastest possible solution for the problem.

Question 6

An algorithm processes an input of size 'n' by dividing it in half repeatedly until the size becomes 1. What is the most likely time complexity of this algorithm?

O(log n)
O(n^2)
O(n log n)
O(n)

Question 7

The programming language you are using.
The development time and resources available.
The size of the datasets your application will handle.
The acceptable performance limits of your application.

Question 8

The O(n log n) algorithm has a large constant factor hidden by Big O notation.
The input data is not random, and the optimized algorithm doesn't handle the specific data patterns well.
You made a mistake in your code optimization.
The benchmarking process is flawed.

Question 9

If an algorithm has a time complexity of Ω(n log n), what can you conclude about its best-case runtime?

It will always be slower than an algorithm with O(n^2) time complexity.
It cannot be faster than an algorithm with O(log n) time complexity.
It will have a constant runtime regardless of input size.
It will always be faster than an algorithm with O(n) time complexity.

Question 10

A linear search algorithm iterates through an unsorted array to find a target element. What is its average-case time complexity?

O(n²)
O(1)
O(n)
O(log n)

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