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- 1. Multiple Choice
Indicate constant time complexity in terms of Big-O notation
O(n)
O(1)
O(log n)
O (n^2)
- 2. Multiple Choice
Indicate exponential time complexity in terms of big-O notation
O (n)
O (n^2)
O (2^n)
O (log n)
- 3. Multiple Choice
Find the slowest time complexity
O (n)
O (n^2)
O (n!)
O (2^n)
- 4. Multiple Choice
Which notation is consistent for every execution?
O (n)
O (n^2)
O (1)
O (2^n)
- 5. Multiple Choice
The O (n!) is so inefficient, there is no practical use for it
True
False
- 6. Multiple Choice
Which notation grows in proportion to the size of the input
O (n)
O (n^2)
O (1)
O (2^n)
- 7. Multiple Choice
Which notation would you usually use for a nested loop?
O (n)
O (n^2)
O (1)
O (2^n)
- 8. Multiple Choice
Indicate polynomial time complexity in terms of big-O notation
O (n!)
O (1)
O (n^2)
O (log n)
- 9. Fill in the Blank
Big-O is used to identify the most ____ algorithm for a specific purpose
- 10. Multiple Choice
When calculating the Big-O for an algorithm, which of the following rules is not true?
Focus on the dominant term
Disregard any constants
Focus only on polynomials
Count the number of assignments
- 11. Multiple Choice
Big-O notations tell you how long the algorithm will take to complete in standard time
True
False
- 12. Multiple Choice
Identify the dominant term in the following algorithm
- 13. Multiple Choice
Identify the dominant term in the following algorithm
n
- 14. Multiple Choice
The number of executions grows extremely quickly as the size of the input increases
Exponential Time
Linear Time
Polynomial Time
Constant Time
- 15. Multiple Choice
The number of executions grows in proportion to the size of the input
Exponential Time
Linear Time
Polynomial Time
Constant Time
- 16. Multiple Choice
The number of executions remains the same regardless of the input size
Exponential Time
Linear Time
Polynomial Time
Constant Time
- 17. Multiple Choice
The number of executions grows quickly by the input being multiplied by the input
Exponential Time
Linear Time
Polynomial Time
Constant Time
- 18. Multiple Choice
No matter how large the input is, the time taken doesn’t change.
Quadratic
Linear
Logarithmic
Constant
Linearithmic
- 19. Multiple Choice
For every element, you are doing a constant number of operations, such as comparing each element to a known value.
Quadratic
Linear
Logarithmic
Constant
Exponential
- 20. Fill in the Blank
When calculating the time complexity of an algorithm, you must focus only on the ____ term
- 21. Fill in the Blank
Big-O is used to calculate the ____ case runtime for an algorithm
- 22. Multiple Choice
The time complexity of an algorithm indicates how much time an algorithm will take to complete
True
False
- 23. Multiple Choice
O is the worst case scenario for
Merge Sort
Bubble Sort
Binary Search
Linear Search
- 24. Multiple Choice
O (n log n) is the worst case scenario for
Merge Sort
Bubble Sort
Binary Search
Linear Search
- 25. Fill in the Blank
O n is the worst case scenario for a ___ ____ algorithm
- 26. Fill in the Blank
O log n is the worst case scenario for a ___ ____ algorithm
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