Python Heapq

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    Question 1

    What is the primary characteristic of a min-heap data structure?

    • The smallest element is at the leaf nodes.

    • Each parent node is larger than its children.

    • The smallest element is always at the root.

    • Elements are stored in a random order.

    Question 2

    Which function in the heapq module is used to add an element while maintaining the heap property?

    • heapq.pop()

    • heapq.insert()

    • heapq.heappush()

    • heapq.add()

    Question 3

    What operation does the heapq.heapreplace() function perform?

    • It only adds an element to the heap.

    • It pops the largest element from the heap.

    • It pops the smallest element and adds a new element.

    • It merges two heaps into one.

    Question 4

    Which of the following functions allows retrieval of the n largest elements from a heap?

    • heapq.nlargest()

    • heapq.getlargest()

    • heapq.maxelements(n)

    • heapq.retrievelargest()

    Question 5

    What is a disadvantage of using a heap queue?

    • It supports random access to elements.

    • It allows efficient sorting of all elements.

    • It is not thread-safe.

    • It requires more memory than linked lists.

    Question 6

    In Python's heapq module, which function is used to merge multiple sorted iterables into a single sorted heap?

    • heapq.combine()

    • heapq.merge()

    • heapq.concat()

    • heapq.join()

    Question 7

    When using the heappop() function, what is the result?

    • It adds a new element to the heap.

    • It returns the largest element in the heap.

    • It removes and returns the smallest element in the heap.

    • It checks the size of the heap.

    Question 8

    Which of the following statements about heaps is true?

    • Heaps can be implemented using binary trees only.

    • Heaps support random access to elements efficiently.

    • Heaps do not allow duplicate elements.

    • Heaps can be implemented using lists in Python.

    Question 9

    What is the time complexity of the heappush() operation in a heap?

    • O(1)

    • O(n)

    • O(log n)

    • O(n log n)

    Question 10

    Which heap operation is more efficient when replacing the smallest element with a new value?

    • Using heappop() followed by heappush()

    • Using heapq.replace()

    • Using heappushpop()

    • Using heapq.merge()

    There are 11 questions to complete.

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