C950 Data Structures and Algorithms II
Access The Exact Questions for C950 Data Structures and Algorithms II
💯 100% Pass Rate guaranteed
🗓️ Unlock for 1 Month
Rated 4.8/5 from over 1000+ reviews
- Unlimited Exact Practice Test Questions
- Trusted By 200 Million Students and Professors
What’s Included:
- Unlock Actual Exam Questions and Answers for C950 Data Structures and Algorithms II on monthly basis
- Well-structured questions covering all topics, accompanied by organized images.
- Learn from mistakes with detailed answer explanations.
- Easy To understand explanations for all students.
Free C950 Data Structures and Algorithms II Questions
Explain how greedy algorithms differ from other algorithm design paradigms in terms of decision-making during problem-solving.
-
Greedy algorithms consider all possible solutions before making a decision.
-
Greedy algorithms make the best local choice at each step without reconsidering previous choices.
-
Greedy algorithms always guarantee the optimal solution.
-
Greedy algorithms require a complete search of the solution space.
Explanation
Correct Answer
B. Greedy algorithms make the best local choice at each step without reconsidering previous choices.
Explanation
Greedy algorithms make a series of decisions by choosing the locally optimal solution at each step, aiming to find a global optimum. These decisions are made based on immediate gains or benefits, without revisiting or revising previous choices. The key characteristic is that greedy algorithms do not backtrack or reconsider previous steps. They are often simpler and faster but do not always guarantee an optimal solution.
Why other options are wrong
A. Greedy algorithms consider all possible solutions before making a decision.
This is incorrect because greedy algorithms do not consider all possible solutions upfront. Instead, they make decisions based on immediate local benefits.
C. Greedy algorithms always guarantee the optimal solution.
Greedy algorithms do not always guarantee the optimal solution. In some cases, a greedy approach may lead to suboptimal results, especially when the problem requires a more complex decision-making process.
D. Greedy algorithms require a complete search of the solution space.
Greedy algorithms do not require a complete search of the solution space. In fact, one of their strengths is that they make decisions step by step, without needing to explore all possibilities, which makes them more efficient for certain types of problems.
It is a linear data structure where each element is a separate object.
-
Pointer
-
Linked List
-
Parameter
-
Array
Explanation
Correct Answer
B. Linked List
Explanation
A linked list is a linear data structure where elements, called nodes, are stored in separate objects, each containing a reference (pointer) to the next element in the sequence. This allows for dynamic memory allocation and easy insertion or deletion of elements. Unlike arrays, linked lists do not require contiguous memory locations, and each node can point to the next.
Why other options are wrong
A. Pointer
A pointer is a variable that stores the address of another variable, often used to reference elements in data structures like linked lists. However, it is not itself a data structure.
C. Parameter
A parameter is a value passed into a function or method. It is not a data structure but rather a concept in programming related to function calls.
D. Array
An array is a collection of elements stored in contiguous memory locations. It is not a linked structure like a linked list, where each element is an object pointing to the next one.
What is the worst case big-O run time for sequential search on an array with n elements?
-
O(n)
-
O(n²)
-
O(n/2)
-
O(log n)
Explanation
Correct Answer
A. O(n)
Explanation
In the worst case, a sequential search must examine every element in the array to find the target (or determine it is not present). This results in a linear number of comparisons, giving it a time complexity of O(n). The search scales proportionally with the number of elements.
Why other options are wrong
B. O(n²)
This runtime suggests nested loops or operations where the number of steps grows quadratically. Sequential search involves a single loop through the array, not multiple nested iterations.
C. O(n/2)
Although on average a sequential search might find the element halfway through the array, Big-O notation ignores constant factors. Thus, O(n/2) simplifies to O(n) in asymptotic analysis and is not the correct way to express the worst case.
D. O(log n)
This applies to algorithms like binary search that divide the search space. Sequential search does not do this—it checks each element one by one—so logarithmic time is not applicable.
Provide the worst-case running time for in-order traversal of a BST tree.
-
O(log n)
-
O(1)
-
O(n)
-
O(n log n)
Explanation
Correct Answer
C. O(n)
Explanation
In-order traversal of a binary search tree (BST) involves visiting each node once in a left-root-right order. Regardless of whether the tree is balanced or skewed, every node must be visited exactly once. Therefore, in the worst case, when the tree is completely unbalanced or even in the best case, the traversal takes linear time — O(n).
Why other options are wrong
A. O(log n)
This is incorrect because O(log n) represents the depth of a balanced BST, not the time it takes to traverse all nodes. Traversal involves visiting every node, not just searching or inserting, so log(n) is an underestimate of the actual time.
B. O(1)
This is incorrect because O(1) suggests constant time regardless of input size. Since in-order traversal requires visiting each of the n nodes, it can never be done in constant time.
D. O(n log n)
This is wrong because it implies that each of the n nodes requires log(n) time to process, which isn't the case. Each node is visited once and in constant time during traversal, leading to a total time of O(n), not O(n log n).
The algorithm fsu::Vector<T>::PushBack(const T& t) has asymptotic runtime
-
Amortized O(size)
-
None of the above
-
O(1)
-
Amortized O(1)
-
O(size)
Explanation
Correct Answer
D. Amortized O(1)
Explanation
The PushBack operation for a dynamic array, like fsu::Vector<T>, typically has an amortized time complexity of O(1). This is because most of the time, the operation involves adding an element to the end of the array in constant time. However, when the array reaches its capacity, it needs to be resized, which takes O(size) time. Since resizing happens less frequently as the array grows, the average cost of each PushBack operation is O(1) over time.
Why other options are wrong
A. Amortized O(size)
This is incorrect because the resizing operation does not happen for every push but rather at specific intervals, so the amortized time complexity does not scale linearly with the size of the vector.
B. None of the above
This is incorrect because amortized O(1) is the correct answer, and it is included in the options.
C. O(1)
This is incorrect because although the PushBack operation is O(1) on average, it can sometimes be O(size) when a resize is necessary. Therefore, it is not O(1) in all cases but rather amortized O(1).
E. O(size)
This is incorrect because the PushBack operation is typically O(1) except during resizing, which happens less frequently, making the overall time complexity amortized O(1).
Which one of the following is not a requirement of an algorithm?
-
Effectiveness
-
Finiteness
-
Definiteness
-
Correctness
Explanation
Correct Answer
D. Correctness
Explanation
While correctness is an important characteristic of an algorithm in practice, it is not a formal requirement of an algorithm as defined in computer science. The key requirements of an algorithm are:
Effectiveness: Every step must be basic enough to be carried out in a finite amount of time by a human or machine.
Finiteness: An algorithm must eventually terminate after a finite number of steps.
Definiteness: The steps of the algorithm must be clearly defined and unambiguous.
Correctness, on the other hand, refers to whether the algorithm produces the correct output for all inputs, which is a goal or property but not a formal requirement in the strict definition of an algorithm.
Why other options are wrong
A. Effectiveness
Effectiveness is a requirement of an algorithm because it ensures that each step of the algorithm is simple and executable, either by a human or a machine.
B. Finiteness
Finiteness is a critical requirement because an algorithm must eventually terminate, ensuring that it doesn't run indefinitely.
C. Definiteness
Definiteness is another fundamental requirement. Each step of the algorithm must be precisely defined, ensuring that there is no ambiguity in the instructions.
What is a tree structure?
-
A way of representing the hierarchical nature of a structure in a graphical form.
-
A way of demonstrating that all living things descended from plants.
-
A way of demonstrating evolution.
-
A logical structure that is only used in Biology.
Explanation
Correct Answer
A. A way of representing the hierarchical nature of a structure in a graphical form.
Explanation
A tree structure is a hierarchical data structure that consists of nodes connected by edges, representing relationships between elements. It is commonly used in computer science to represent various types of hierarchical data, such as organizational charts, file systems, and family trees. The tree starts with a root node, and each node can have child nodes, forming a branching structure.
Why other options are wrong
B. A way of demonstrating that all living things descended from plants.
This is unrelated to the concept of a tree structure in computer science. It refers to a biological concept of evolutionary relationships, not a data structure.
C. A way of demonstrating evolution.
While trees can represent evolutionary relationships (evolutionary trees), this is not the focus of the data structure tree used in computing. Evolutionary trees are a biological concept, not a computational one.
D. A logical structure that is only used in Biology.
This is incorrect because tree structures are widely used in computing, not just biology. They represent hierarchical relationships in many different contexts, such as computer file systems, databases, and more.
Explain why definiteness is an important characteristic of algorithms. How does it contribute to the effectiveness of an algorithm?
-
It ensures that algorithms can be executed in any programming language.
-
It allows for multiple interpretations of the instructions.
-
It guarantees that the algorithm can be understood and executed without confusion.
-
It simplifies the algorithm by reducing the number of steps.
Explanation
Correct Answer
C. It guarantees that the algorithm can be understood and executed without confusion.
Explanation
Definiteness refers to the clarity of the steps involved in an algorithm. Every step in the algorithm must be precisely defined, leaving no ambiguity in its execution. This ensures that the algorithm can be executed correctly and efficiently, and can be implemented without confusion. Without definiteness, an algorithm may be misinterpreted or fail to execute as intended, leading to incorrect results or inefficiencies.
Why other options are wrong
A. It ensures that algorithms can be executed in any programming language.
While definiteness is crucial for the correct execution of an algorithm, it does not ensure that an algorithm can be executed in any programming language. An algorithm's implementation may depend on the language used, but definiteness makes sure the algorithm's logic is clear regardless of the language.
B. It allows for multiple interpretations of the instructions.
This is the opposite of definiteness. Definiteness ensures that the instructions are precise and not open to interpretation.
D. It simplifies the algorithm by reducing the number of steps.
Definiteness doesn't necessarily simplify an algorithm or reduce its steps. It focuses on making the algorithm's steps clear and unambiguous, regardless of their complexity or number.
Why do ADTs improve programmer efficiency?
-
They allow focusing on higher-level operations and algorithms
-
They require understanding the underlying data structures
-
They involve complicated implementations
-
They need frequent updating of code
Explanation
Correct Answer
A. They allow focusing on higher-level operations and algorithms
Explanation
Abstract Data Types (ADTs) improve programmer efficiency because they allow programmers to focus on higher-level operations and algorithms without needing to worry about the implementation details of the underlying data structures. ADTs provide an abstraction that hides the complexity of data structure implementation, making it easier to work with data and solve problems.
Why other options are wrong
B. They require understanding the underlying data structures
While understanding the underlying data structures is useful, the main benefit of ADTs is that they abstract away these details, allowing the programmer to focus on the problem at hand rather than on the data structure itself.
C. They involve complicated implementations
ADTs themselves are designed to simplify implementation by abstracting the complexity of data structures. They do not necessarily involve complicated implementations, but rather, they provide a simpler interface to interact with the data.
D. They need frequent updating of code
ADTs, once defined, provide a stable interface for interaction. They do not necessarily require frequent updating of code. The purpose of an ADT is to provide a reliable and consistent way of interacting with data.
Explain the significance of the public and private components in an Abstract Data Type (ADT). How do they contribute to the overall functionality of the ADT?
-
Public components allow external access, while private components protect internal data.
-
Private components are only for internal use, while public components are used for data storage.
-
Public components are for data manipulation, while private components are for user interaction.
-
Both components serve the same purpose in an ADT.
Explanation
Correct Answer
A. Public components allow external access, while private components protect internal data.
Explanation
In an Abstract Data Type (ADT), public components are the methods and functions that are accessible to the outside world. They define the interface through which users interact with the data structure, enabling them to perform operations like insert, delete, push, pop, or traverse depending on the ADT. On the other hand, private components refer to the internal structure and implementation details that are hidden from the user. These private elements protect the integrity of the data and ensure that the ADT operates in a controlled and predictable manner, preventing external manipulation that could lead to inconsistencies or errors.
Why other options are wrong
B. Private components are only for internal use, while public components are used for data storage.
This is incorrect because private components are not used for data storage but for internal logic and management of the data. Public components provide access to the data but are not used for direct storage.
C. Public components are for data manipulation, while private components are for user interaction.
This is incorrect because public components are used for interaction with the ADT and manipulation of its data, while private components handle the internal workings of the ADT, not user interaction.
D. Both components serve the same purpose in an ADT.
This is incorrect because public and private components serve different roles. Public components provide an interface for external use, while private components handle the internal implementation details of the ADT.
How to Order
Select Your Exam
Click on your desired exam to open its dedicated page with resources like practice questions, flashcards, and study guides.Choose what to focus on, Your selected exam is saved for quick access Once you log in.
Subscribe
Hit the Subscribe button on the platform. With your subscription, you will enjoy unlimited access to all practice questions and resources for a full 1-month period. After the month has elapsed, you can choose to resubscribe to continue benefiting from our comprehensive exam preparation tools and resources.
Pay and unlock the practice Questions
Once your payment is processed, you’ll immediately unlock access to all practice questions tailored to your selected exam for 1 month .