Why Heap Exists and What Sorted Array Cannot Do Efficiently in DSA C++ - Test Your Knowledge

Practice - 5 Tasks

1fill in blank

easy

Complete the code to insert a new element into a max heap.

DSA C++

void insert(int heap[], int& size, int value) {
    size++;
    heap[size] = value;
    int i = size;
    while (i > 1 && heap[i] > heap[[1]]) {
        std::swap(heap[i], heap[i / 2]);
        i = i / 2;
    }
}

Drag options to blanks, or click blank then click option'

Asize

Bi - 1

Ci + 1

Di / 2

Attempts:

3 left

💡 Hint

Common Mistakes

Using i - 1 or i + 1 instead of i / 2 to find the parent index.

Comparing with the wrong index causing incorrect heap structure.

In a heap stored as an array, the parent of node at index i is at index i / 2. This is why we compare heap[i] with heap[i / 2] to maintain the max heap property.

2fill in blank

medium

Complete the code to extract the maximum element from a max heap.

DSA C++

int extractMax(int heap[], int& size) {
    if (size == 0) return -1;
    int maxVal = heap[1];
    heap[1] = heap[size];
    size--;
    int i = 1;
    while (true) {
        int left = 2 * i;
        int right = 2 * i + 1;
        int largest = i;
        if (left <= size && heap[left] > heap[[1]]) largest = left;
        if (right <= size && heap[right] > heap[largest]) largest = right;
        if (largest == i) break;
        std::swap(heap[i], heap[largest]);
        i = largest;
    }
    return maxVal;
}

Drag options to blanks, or click blank then click option'

Aright

Bleft

Ci

Dsize

Attempts:

3 left

💡 Hint

Common Mistakes

Comparing left child with right child instead of current node.

Using wrong indices causing heap property violation.

We compare the left child heap[left] with the current node heap[i] to find the largest value among them. This helps in maintaining the max heap property during extraction.

3fill in blank

hard

Fix the error in the code that checks if a sorted array can efficiently support insertion.

DSA C++

bool canInsertEfficiently(const std::vector<int>& sortedArr) {
    // Insertion in sorted array requires shifting elements
    // Time complexity is O([1])
    return false;
}

Drag options to blanks, or click blank then click option'

A1

Bn

Clog n

Dn log n

Attempts:

3 left

💡 Hint

Common Mistakes

Assuming insertion is logarithmic time in a sorted array.

Confusing search time with insertion time.

Insertion in a sorted array requires shifting elements to maintain order, which takes linear time O(n). This is inefficient compared to heap insertion.

4fill in blank

hard

Fill both blanks to complete the code that compares insertion time in a sorted array and a heap.

DSA C++

int insertionTimeSortedArray(int n) {
    return n * [1]; // shifting elements
}

int insertionTimeHeap(int n) {
    return [2]; // heap insertion time
}

Drag options to blanks, or click blank then click option'

A1

Blog n

Cn

Dn log n

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'n' for heap insertion time instead of 'log n'.

Using 'log n' for shifting cost instead of constant time per element.

In a sorted array, insertion requires shifting n elements by 1 step each, so time is proportional to n * 1 = n. In a heap, insertion takes O(log n) time due to heapify operations.

5fill in blank

hard

Fill all three blanks to complete the code that demonstrates why heaps are better for priority queue operations than sorted arrays.

DSA C++

struct PriorityQueue {
    std::vector<int> data;

    void insert(int value) {
        data.push_back(value);
        int i = data.size() - 1;
        while (i > 0 && data[i] > data[[1]]) {
            std::swap(data[i], data[[2]]);
            i = [3];
        }
    }
};

Drag options to blanks, or click blank then click option'

Ai / 2

Bi - 1

C(i - 1) / 2

Di + 1

Attempts:

3 left

💡 Hint

Common Mistakes

Using one-based indexing formula i / 2 instead of zero-based.

Using incorrect parent index causing infinite loops.

In a heap implemented as a vector, the parent of node at index i is at index (i - 1) / 2. This is used to maintain the heap property during insertion.

Practice

1. Why do we use a heap instead of just a sorted array for priority queue operations?

easy

A. Because heaps use less memory than arrays

B. Because heaps allow faster insertion and deletion of the highest priority element

C. Because heaps store elements in sorted order permanently

D. Because heaps do not allow duplicate elements

Solution

1. Step 1: Understand insertion and deletion in sorted arrays

   Inserting or deleting an element in a sorted array requires shifting elements, which takes O(n) time.

2. Step 2: Compare with heap operations

   Heaps allow insertion and deletion of the highest priority element in O(log n) time by maintaining a tree structure.

3. Final Answer:

   Because heaps allow faster insertion and deletion of the highest priority element -> Option B

4. Quick Check:

   Heap operations faster than sorted array for insert/delete [OK]

Hint: Heaps speed up insert/delete compared to sorted arrays [OK]

Common Mistakes:

• Thinking heaps keep elements fully sorted
• Assuming heaps use less memory than arrays
• Believing heaps disallow duplicates

2. Which of the following is the correct way to insert an element into a max heap in C++ using STL?

easy

A. std::push_heap(v.begin(), v.end())

B. std::push_heap(v.begin(), v.end(), std::greater<int>())

C. std::push_heap(v.begin(), v.end(), std::less<int>())

D. std::push_heap(v.begin(), v.end(), std::equal_to<int>())

Solution

1. Step 1: Recall default heap behavior in C++ STL

   By default, std::push_heap creates a max heap using std::less comparator implicitly.

2. Step 2: Check the correct syntax for push_heap

   std::push_heap(v.begin(), v.end()) is the correct call to insert the last element into the max heap.

3. Final Answer:

   std::push_heap(v.begin(), v.end()) -> Option A

4. Quick Check:

   Default max heap uses push_heap without comparator [OK]

Hint: Use push_heap without comparator for max heap [OK]

Common Mistakes:

• Using std::greater instead of default std::less
• Passing wrong comparator to push_heap
• Confusing push_heap with push_back

3. Consider the following C++ code snippet:

std::vector<int> v = {10, 20, 15};
std::make_heap(v.begin(), v.end());
v.push_back(30);
std::push_heap(v.begin(), v.end());
v.pop_back();
std::pop_heap(v.begin(), v.end());
for (int x : v) std::cout << x << " ";

What is the output?

medium

A. 30 20 10

B. 30 20 15

C. 20 15 10 30

D. 20 15 30

Solution

1. Step 1: Build initial max heap from {10, 20, 15}

   After std::make_heap, the vector becomes a max heap: {20, 10, 15}.

2. Step 2: Add 30 and push_heap

   After push_back(30) and push_heap, 30 moves to root: {30, 20, 15, 10}.

3. Step 3: pop_back and pop_heap

   pop_back removes last element (10), vector becomes {30, 20, 15}, then pop_heap swaps max to end {15, 20, 30} and heapifies the front: {20, 15, 30}.

4. Step 4: Output vector elements

   After pop_heap, the vector is {20, 15, 30}. The for loop prints all elements including the swapped max at the end, so output is "20 15 30 ".

5. Final Answer:

   20 15 30 -> Option D

6. Quick Check:

   Heap after pop_heap = 20 15 30 [OK]

Hint: Remember pop_heap swaps max to end before pop_back [OK]

Common Mistakes:

• Ignoring that pop_heap swaps max to end
• Forgetting to call push_heap after push_back
• Assuming vector size unchanged after pop_back

4. The following code attempts to maintain a min heap using C++ STL heap functions:

std::vector<int> v = {5, 10, 15};
std::make_heap(v.begin(), v.end());
v.push_back(3);
std::push_heap(v.begin(), v.end());

But the smallest element is not at the front after these operations. What is the error?

medium

A. std::make_heap and std::push_heap create max heaps by default, not min heaps

B. The vector must be sorted before calling make_heap

C. push_back should be called after push_heap

D. The vector should be empty before calling make_heap

Solution

1. Step 1: Understand default heap behavior in C++ STL

   By default, make_heap and push_heap create max heaps, so smallest element won't be at front.

2. Step 2: Identify missing comparator for min heap

   To create a min heap, a comparator like std::greater<int> must be passed to make_heap and push_heap.

3. Final Answer:

   std::make_heap and std::push_heap create max heaps by default, not min heaps -> Option A

4. Quick Check:

   Default heap is max heap, need comparator for min heap [OK]

Hint: Default STL heaps are max heaps; add comparator for min heap [OK]

Common Mistakes:

• Assuming make_heap sorts the vector
• Calling push_heap before push_back
• Thinking vector must be empty before make_heap

5. You have a large stream of numbers arriving one by one. You want to always quickly find the median after each insertion. Why is a heap-based approach better than keeping a fully sorted array?

hard

A. Because heaps use less memory than arrays

B. Because heaps keep all elements sorted internally

C. Because heaps allow insertion in O(log n) and median retrieval in O(1) using two heaps

D. Because arrays cannot store large data

Solution

1. Step 1: Understand median finding with sorted array

   Maintaining a sorted array requires O(n) insertion time to keep order after each new number.

2. Step 2: Understand median finding with two heaps

   Using two heaps (max heap for lower half, min heap for upper half) allows O(log n) insertion and O(1) median retrieval.

3. Final Answer:

   Because heaps allow insertion in O(log n) and median retrieval in O(1) using two heaps -> Option C

4. Quick Check:

   Two heaps optimize median updates efficiently [OK]

Hint: Two heaps give fast median updates, arrays are slow [OK]

Common Mistakes:

• Thinking heaps keep all elements sorted
• Assuming arrays are always faster
• Confusing memory use with speed