Imagine you have a sorted array, and you want to find the index of the first element that is greater than a given target value. Which algorithm would provide the most efficient solution?
Linear Search
Binary Search
Bubble Sort
Selection Sort
You need to sort a massive dataset of social media posts by timestamp. The timestamps are represented as long integers. Which sorting approach is likely the MOST efficient?
Bucket Sort with buckets representing time intervals (e.g., hours, days)
Radix Sort using a base that aligns with the structure of the timestamps
Any of the above would be equally efficient for this scenario
External Merge Sort, as timestamps are naturally comparable
In the context of external sorting, what does the term 'run' typically refer to?
A single pass through the entire dataset.
A sequence of sorted elements that can be held in memory.
The process of merging two sorted subarrays.
The number of disk I/O operations performed.
When is Bucket Sort LEAST likely to be an efficient sorting algorithm?
The data is heavily skewed towards a few buckets.
The dataset is very large and sparse.
The data is uniformly distributed.
The elements are integers within a known range.
Which data structure would be the MOST suitable for implementing a sparse matrix efficiently, where most elements are zero, to save memory and potentially improve computation speed?
Linked List
Hash Table
Multidimensional Array
Binary Tree
Which sorting algorithm is the MOST suitable for sorting a massive dataset that cannot fit entirely in RAM?
External Merge Sort
Merge Sort
Quick Sort
Which sorting algorithm is generally considered IN-PLACE, meaning it requires minimal additional memory overhead?
Radix Sort (using counting sort as a subroutine)
Bucket Sort
External Merge Sort is particularly well-suited for scenarios where:
Real-time sorting is required.
The elements are already nearly sorted.
The dataset is small and fits entirely in memory.
The data is stored on a slow, disk-based storage device.
In a real-world application, you are using a dynamic array to store a constantly growing dataset. You notice that the performance degrades significantly during the array resizing operations. What strategy could you employ to mitigate this performance bottleneck?
Implement a custom memory allocator that reserves larger chunks of contiguous memory in advance.
Optimize the algorithm that processes the data to reduce the overall number of insertions into the array.
Increase the frequency of resizing, reallocating the array with smaller size increments.
Switch to a linked list data structure, sacrificing some element access speed for better insertion performance.
Given an array of integers, find the subarray with the maximum sum in linear time. The subarray must contain at least one element.
Kadane's Algorithm, which iterates through the array, keeping track of the maximum sum ending at each index.
Divide and conquer approach by finding the maximum subarray in the left half, right half, and the one crossing the midpoint.
Brute force approach by checking all possible subarrays.
Dynamic programming approach by building a table to store the maximum sum ending at each index.