What is a significant disadvantage of using arrays for storing and processing extremely large datasets, particularly in the context of limited memory resources?
Arrays are not suitable for storing structured data, such as key-value pairs.
Arrays have slow access times for individual elements.
Arrays do not support dynamic resizing, making it challenging to handle growing datasets.
Arrays require contiguous blocks of memory, which can be difficult to allocate for massive datasets.
Given an array of n positive integers and a positive integer s, find the minimal length of a contiguous subarray of which the sum ≥ s. If there isn't one, return 0 instead.
Use two nested loops to iterate through all possible subarrays.
Use a sliding window approach to find the minimal length subarray.
Use dynamic programming to store the minimal length for all subarrays ending at each index.
Use binary search to find the minimal length.
Radix Sort operates by:
Recursively dividing the array and sorting subarrays.
Comparing elements and swapping them based on their values.
Distributing elements into buckets based on individual digits or characters.
Building a binary tree and performing an in-order traversal.
Given an array containing n distinct numbers taken from 0, 1, 2, ..., n, find the missing number.
Use the XOR operation to find the missing number.
Sort the array and find the missing element.
Calculate the sum of all numbers from 0 to n and subtract the sum of the array elements.
Use a hash table to store the presence of each number.
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.
Switch to a linked list data structure, sacrificing some element access speed for better insertion performance.
Increase the frequency of resizing, reallocating the array with smaller size increments.
In the context of amortized analysis, what is the purpose of the potential function?
To analyze the space complexity of an algorithm.
To optimize the performance of individual array operations.
To calculate the average runtime of a single operation over a sequence of operations.
To determine the maximum possible runtime of a single operation in the worst-case scenario.
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?
Any of the above would be equally efficient for this scenario
External Merge Sort, as timestamps are naturally comparable
Bucket Sort with buckets representing time intervals (e.g., hours, days)
Radix Sort using a base that aligns with the structure of the timestamps
When is Bucket Sort LEAST likely to be an efficient sorting algorithm?
The elements are integers within a known range.
The data is heavily skewed towards a few buckets.
The data is uniformly distributed.
The dataset is very large and sparse.
Given an array of n integers, find three elements in the array such that the sum is closest to a given target number. Return the sum of the three integers.
Use a hash table to store the sum of all pairs of elements.
Sort the array and use two pointers to find pairs of elements with a sum close to the target minus the current element.
Use three nested loops to iterate through all possible triplets.
Use dynamic programming to store the closest sum for all subarrays of size three.
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?
Selection Sort
Binary Search
Linear Search
Bubble Sort