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A binary heap is a tree-based data structure that is commonly used to implement priority queues . A binary heap allows efficient insertion of elements and extraction of the minimum or maximum element, depending on whether it is a min-heap or max-heap. Both operations take logarithmic time, making binary heaps optimal for scenarios where priority-based processing is needed, such as in scheduling algorithms or Dijkstra’s shortest path algorithm. A (Incorrect): A stack is a last-in, first-out (LIFO) structure, which is not suitable for maintaining element priorities. B (Incorrect): A queue is a first-in, first-out (FIFO) structure that processes elements in the order they arrive, without considering priority. C (Incorrect): A linked list could be used to implement a priority queue, but its performance would be less efficient than a binary heap, as it requires linear time for insertion and extraction. E (Incorrect): A hash table provides fast lookups but does not maintain any order, making it unsuitable for a priority queue.
(11.98% of 449.99) - 3.998 = √?
90.004% of 9500 + 362 = ?
25, 28, 26, 29, 27, ?
(√780 + 111.98) ÷ 6.95 + 39.95% of 179.98 = ?
104.27% of 1200.11 + 12.08% of 2360.81 = 22.23% of ? + 1430.99
`11(2/13)` + `5(2/11)` - `3(4/9)` = ?
(9116.89 – 8024.89 + 902.95) × 14 = 1800 × ?
(5.013 – 30.04) = ? + 11.98% of 4799.98
Find the approximate value of Question mark(?). No need to find the exact value.
(34.95 × 7.03) ÷ 5 + 27.98% of 249.88 – √(80.81) = ?
...11.69% of 499.78 + (2.89 × 39.76) = ?