Array manipulation involves operations such as _______ and _______ to modify array elements.
- Concatenation, rotation
- Insertion, deletion
- Sorting, searching
- Traversal, deletion
Array manipulation involves operations such as insertion and deletion to modify array elements. Insertion adds elements at a specific position, and deletion removes elements from a given position, helping to manage the array's content dynamically.
Topological sorting is often used in _______ resolution, particularly in systems involving tasks with dependencies.
- Conflict
- Dependency
- Priority
- Scheduling
Topological sorting is often used in Scheduling resolution, particularly in systems involving tasks with dependencies. It helps in determining the order of execution for tasks based on their dependencies, ensuring a systematic and correct execution flow.
An array is a _______ structure that stores a collection of _______ elements.
- Linear, Heterogeneous
- Linear, Homogeneous
- Non-linear, Heterogeneous
- Non-linear, Homogeneous
An array is a linear structure that stores a collection of homogeneous elements. It means that all elements in the array are of the same data type.
Linear search is _______ efficient for searching large datasets.
- Extremely
- Highly
- Moderately
- Not very
Linear search is not very efficient for searching large datasets. Since it checks each element sequentially, it may take a long time to find the desired element in a large dataset, making it less suitable for scenarios where efficiency is crucial.
Proper memory management in arrays involves _______ memory when it is no longer needed.
- Automatically releasing
- Explicitly allocating
- Restricting access to
- Storing in a separate cache
Proper memory management in arrays involves automatically releasing memory when it is no longer needed. This process, known as deallocation or freeing memory, prevents memory leaks and ensures efficient memory usage.
What is the objective of finding the longest common subsequence?
- To find the maximum length subarray that is common to two sequences.
- To find the maximum length subsequence that is common to two or more sequences.
- To identify the shortest common sequence between two sequences.
- To minimize the length of the common subsequence.
The objective of finding the longest common subsequence is to determine the maximum length subsequence that is common to two or more sequences. It is often used in applications like DNA sequence comparison and version control systems.
Rabin-Karp algorithm is particularly useful when _______.
- Dealing with sorted arrays.
- Pattern matching involves numeric data.
- Searching for a single pattern in multiple texts.
- There are multiple occurrences of the pattern in the text.
The Rabin-Karp algorithm is particularly useful when searching for a single pattern in multiple texts. It employs hashing to efficiently search for a pattern in a text, making it advantageous in scenarios where the same pattern needs to be matched across different texts.
Insertion Sort is particularly effective when the input array is nearly _______ sorted.
- Completely
- Partially
- Randomly
- Sequentially
Insertion Sort is particularly effective when the input array is nearly partially sorted. In such cases, the number of comparisons and swaps required is significantly reduced, making it efficient.
Suppose you are tasked with sorting a small array of integers, where most elements are already sorted in ascending order. Which sorting algorithm would be most suitable for this scenario and why?
- Insertion Sort
- Merge Sort
- Quick Sort
- Selection Sort
Insertion Sort would be the most suitable algorithm for this scenario. It has an average-case time complexity of O(n), making it efficient for small arrays, especially when elements are mostly sorted. Its linear time complexity in nearly sorted arrays outperforms other algorithms.
Suppose you are faced with a scenario where the coin denominations are arbitrary and not necessarily sorted. How would you modify the dynamic programming solution to handle this situation?
- Convert the problem into a graph and apply Dijkstra's algorithm.
- Modify the dynamic programming approach to handle arbitrary denominations without sorting.
- Sort the coin denominations in descending order before applying dynamic programming.
- Use a different algorithm such as quicksort to sort the denominations during runtime.
To handle arbitrary and unsorted coin denominations, you would modify the dynamic programming solution by ensuring that the algorithm considers all possible denominations for each subproblem. Sorting is not necessary; instead, the algorithm dynamically adjusts to the available denominations, optimizing the solution for each specific scenario.