Q. In the 0/1 Knapsack problem, what does dynamic programming help to optimize?
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A.
The number of items
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B.
The weight of the knapsack
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C.
The total value of items
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D.
The arrangement of items
Solution
Dynamic programming helps to optimize the total value of items that can be carried in the knapsack without exceeding its weight limit.
Correct Answer:
C
— The total value of items
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Q. What is the main characteristic of problems suitable for dynamic programming?
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A.
They can be solved in linear time
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B.
They can be divided into smaller subproblems
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C.
They require sorting of data
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D.
They have unique solutions
Solution
Problems suitable for dynamic programming can be divided into smaller subproblems that can be solved independently and combined to form a solution.
Correct Answer:
B
— They can be divided into smaller subproblems
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Q. What is the space complexity of a typical dynamic programming solution that uses a 2D table?
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A.
O(1)
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B.
O(n)
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C.
O(n^2)
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D.
O(n log n)
Solution
The space complexity of a typical dynamic programming solution that uses a 2D table is O(n^2), where n is the size of the input.
Correct Answer:
C
— O(n^2)
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Q. Which dynamic programming problem involves finding the longest increasing subsequence?
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A.
Longest Common Subsequence
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B.
Edit Distance
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C.
Longest Increasing Subsequence
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D.
Matrix Chain Multiplication
Solution
The problem of finding the longest increasing subsequence is a classic example of a problem that can be solved using dynamic programming.
Correct Answer:
C
— Longest Increasing Subsequence
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Q. Which of the following is a common approach to implement dynamic programming?
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A.
Top-down with memoization
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B.
Bottom-up tabulation
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C.
Both top-down and bottom-up
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D.
None of the above
Solution
Both top-down with memoization and bottom-up tabulation are common approaches to implement dynamic programming.
Correct Answer:
C
— Both top-down and bottom-up
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