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## Algorithm Complexity - An algorithm's time complexity is a measure of how long it takes to execute relative to its input size. **True** - An algorithm is always the most efficient solution to a problem. **False** (An algorithm can be inefficient, and not all algorithms are optimal.) - A recursive...

## Algorithm Complexity - An algorithm's time complexity is a measure of how long it takes to execute relative to its input size. **True** - An algorithm is always the most efficient solution to a problem. **False** (An algorithm can be inefficient, and not all algorithms are optimal.) - A recursive algorithm is one that solves the problem by breaking it down into smaller instances of the same problem. **True** - Greedy algorithms always provide the optimal solution to a problem. **False** (Greedy algorithms do not always yield the optimal solution but they work well for certain problems.) - An algorithm that runs in O(1) time is considered more efficient than one that runs in O(n) time. **True** (O(1) means constant time which is faster than O(n), where time increases linearly with input size.) - Sorting algorithms like Merge Sort and Quick Sort have the same worst-case time complexity. **False** (Merge Sort has a worst-case time complexity of O(n log n), while Quick Sort has a worst-case time complexity of O(n²).) - All algorithms can be optimized to run in O(1) time. **False** (Not all algorithms can achieve constant time complexity, it depends on the problem's nature.) ## Time Complexity - Time complexity measures the amount of memory an algorithm uses. **False** (Time complexity measures the amount of time an algorithm takes to run, not the memory usage.) - Space complexity refers to the amount of memory an algorithm needs to run. **True** - An algorithm with a time complexity of O(n) is faster than one with O(n log n) for large input sizes. **False** (O(n log n) is more efficient than O(n²) for large inputs.) - The Big-O notation describes the worst-case time complexity of an algorithm. **True** - An algorithm with constant time complexity, O(1), is always the most efficient. **False** (O(1) is efficient, but it depends on the nature of the problem. Some problems require more complex time complexities.) - Time complexity is always more important than space complexity. **False** (It depends on the problem. In some cases, space complexity is more crucial such as in memory-constrained environments.) - The best-case time complexity of an algorithm is the time it takes under the most favorable input conditions. **True** - Algorithms with exponential time complexity, O(2^n), are generally considered inefficient for large inputs. **True** (Exponential time complexity grows very fast making it impractical for large inputs.) - Amortized time complexity accounts for the average performance of an algorithm over a sequence of operations. **True** - If an algorithm has a time complexity of O(n!), it is considered very efficient. **False** (O(n!) is extremely inefficient and grows faster than even exponential time complexity.) ## Classification of Data Structures - Identify the type of data structure that organizes data in a linear fashion and allows access by index. **Array** - Identify the data structure that follows the First-In-First-Out (FIFO) principle. **Queue** - Name the data structure where each element points to the next, creating a chain of elements. **Linked List** - Identify the non-linear data structure that is used to represent hierarchical relationships between elements. **Tree** - Name the data structure that stores unique elements without any specific order and provides constant-time performance for basic operations like insertion and lookup. **HashSet** - Identify the data structure that allows elements to be added or removed only from one end, following the Last-In-First-Out (LIFO) principle. **Stack** - What is the name of the data structure used to efficiently map unique keys to corresponding values? **HashMap** - Name the linear data structure that supports efficient insertion and deletion from both ends. **Deque (Double-ended Queue)** - Identify the data structure that ensures its elements are arranged in a sorted order, based on their key values. **TreeMap** - What is the type of data structure that can be implemented using either arrays or linked lists commonly used for managing recursive function calls? **Stack**