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Question 1

Which of the following statements is true about space complexity?

A

Space complexity only considers the space taken by input variables.

B

Space complexity measures the total memory used by an algorithm, including output, and auxiliary space.

C

Space complexity is independent of the input size.

D

Space complexity is only important for recursive algorithms.

Question 2

What is auxiliary space?

A

The space taken by the input itself.

B

Extra space or temporary space used by an algorithm, excluding the input.

C

The total space taken by an algorithm, including input.

D

The space taken by the output of an algorithm.

Question 3

What is the space complexity of the following code?

public void printArray(int[] arr) {
    for (int i = 0; i < arr.length; i++) {
        System.out.println(arr[i]);
    }
}

A

O(1)

B

O(n)

C

O(n²)

D

O(log n)

Question 4

What is the space complexity of the following recursive function?

public int factorial(int n) {
    if (n == 0) return 1;
    return n * factorial(n - 1);
}

A

O(1)

B

O(log n)

C

O(n²)

D

O(n)

Question 5

Analyze the space complexity of the following code:

public int uniquePaths(int m, int n) {
    int[][] dp = new int[m][n];
    for (int i = 0; i < m; i++) {
        dp[i][0] = 1;
    }
    for (int j = 0; j < n; j++) {
        dp[0][j] = 1;
    }
    for (int i = 1; i < m; i++) {
        for (int j = 1; j < n; j++) {
            dp[i][j] = dp[i - 1][j] + dp[i][j - 1];
        }
    }
    return dp[m - 1][n - 1];
}

A

O(m * n)

B

O(m + n)

C

O(n)

D

O(1)

Question 6

What is the space complexity of the following code?

public int[] calculatePrefixSum(int[] arr) {
    int[] prefixSum = new int[arr.length];
    prefixSum[0] = arr[0];

    for (int i = 1; i < arr.length; i++) {
        prefixSum[i] = prefixSum[i - 1] + arr[i];
    }
    return prefixSum;
}

A

O(1)

B

O(n²)

C

O(n)

D

O(log n)

.....

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