C949 Data Structures and Algorithms I
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Free C949 Data Structures and Algorithms I Questions
A unique key can _____.
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identify a set of rows
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be shared among several rows
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uniquely identify a row
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be unique for each column
Explanation
Correct Answer
C. uniquely identify a row
Explanation
A unique key in a database is specifically designed to uniquely identify a row in a table. This ensures that no two rows in the table can have the same value for this key, making it possible to distinguish each row based on that key. It is a fundamental concept in database normalization and helps maintain data integrity.
Why other options are wrong
A. identify a set of rows
This is incorrect because a unique key is intended to identify a single row, not a set of rows. If multiple rows share the same value, that would violate the uniqueness constraint.
B. be shared among several rows
This is incorrect because a unique key must not be shared among rows. If it were shared, it would no longer be unique, and its purpose as an identifier would be compromised.
D. be unique for each column
This is incorrect because a unique key can apply to a single column or a combination of columns, but it is not necessary to have a unique key for each column in a table. A unique key applies at the row level, not the column level.
What method is used to clear all entries from a dictionary in programming?
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clear()
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removeAll()
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delete()
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empty()
Explanation
Correct Answer
A. clear()
Explanation
In most programming languages, the clear() method is used to remove all entries from a dictionary. This method empties the dictionary while maintaining the dictionary object itself. It is a common and standardized way of clearing a dictionary in languages like Python and JavaScript.
Why other options are wrong
B. removeAll()
This is incorrect because removeAll() is not a standard method for clearing a dictionary. It may be used in other contexts or languages but not specifically for dictionaries.
C. delete()
This is incorrect because delete() is typically used in languages like JavaScript to remove specific keys or objects, not to clear an entire dictionary or collection. In some languages like Python, del is used to remove specific elements, but it doesn’t clear the whole dictionary.
D. empty()
This is incorrect because empty() is not a standard method for clearing dictionaries in most programming languages. It may be used in some programming contexts but does not apply to dictionaries in the most common languages.
A binary tree has at most two children called
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the left child
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both the left and right child
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None of these options
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the right child
Explanation
Correct Answer
B. both the left and right child
Explanation
A binary tree is a type of data structure in which each node has at most two children. These children are typically referred to as the left child and the right child. The structure ensures that each node can have zero, one, or two children, which is the key property of binary trees.
Why other options are wrong
A. the left child
This option is incomplete, as a binary tree can have both a left and a right child for any given node. It’s not limited to only a left child.
C. None of these options
This option is incorrect, as the correct answer is the two children of a binary tree: the left child and the right child.
D. the right child
This option is also incomplete for the same reason as option A. A binary tree node has both a left and a right child, not just a right child.
What is the correct method to delete a node from a binary tree that has only one child?
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Replace the node with its child
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Delete the child node
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Promote the parent node
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Move the child to the root
Explanation
Correct Answer
A. Replace the node with its child
Explanation
When deleting a node with only one child in a binary tree, the standard procedure is to replace the node with its only child. This maintains the structure and ordering of the tree without needing to reorganize unrelated nodes.
Why other options are wrong
B. Delete the child node
This is incorrect because the child node is the only descendant of the node being deleted. Removing it would discard important data and break the structure of the tree.
C. Promote the parent node
This is incorrect because the parent node is already above the node being deleted. Promotion applies when replacing the deleted node with a child or descendant, not the reverse.
D. Move the child to the root
This is incorrect because moving a single child to the root would disrupt the entire structure of the tree. Only one node is being removed, so such a drastic move is unnecessary and incorrect.
In which tree traversal method is the current node processed after its children have been visited?
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In-order
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Post-order
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Pre-order
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Level-order
Explanation
Correct Answer
B. Post-order
Explanation
In post-order traversal, the current node is processed only after all of its children have been visited. This means the left child is processed first, followed by the right child, and finally, the parent node is processed. Post-order traversal is particularly useful for operations such as deleting nodes or evaluating expressions in expression trees.
Why other options are wrong
A. In-order
In in-order traversal, the current node is processed between its left and right children. The left child is processed first, then the node itself, followed by the right child. This is not the case where the current node is processed after its children, as in post-order.
C. Pre-order
In pre-order traversal, the current node is processed before its children. The node is processed first, then the left child, followed by the right child. This is the opposite of post-order, where the node is processed after its children.
D. Level-order
In level-order traversal, the nodes are processed level by level, starting from the root and moving down through the tree. This method processes the node itself before its children, similar to pre-order, but it processes nodes level by level rather than recursively by subtrees.
What does a vertex or node represent in a graph?
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An item
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An edge
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A connection
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A path
Explanation
Correct Answer
A. An item
Explanation
In a graph, a vertex (or node) represents an individual entity or item, often associated with some data. The edges in the graph represent the connections or relationships between these vertices. A vertex can represent anything depending on the context, such as a city in a map, a user in a social network, or a state in a state machine.
Why other options are wrong
B. An edge
An edge in a graph represents a connection or relationship between two vertices. It is not the same as a vertex.
C. A connection
While vertices are connected by edges, a vertex itself does not represent a connection; it represents an item or entity.
D. A path
A path in a graph is a sequence of edges that connect vertices. A vertex itself is not a path but an element within the graph.
In an N Tree ADT, what is the height of a node?
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The number of edges between the root and the deepest leaf.
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The number of edges between the node and the closest leaf.
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The number of edges between the node and the farthest leaf.
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The number of edges between the node and the tree root.
Explanation
Correct Answer
C. The number of edges between the node and the farthest leaf.
Explanation
In an N-ary tree (or any tree), the height of a node is defined as the number of edges on the longest downward path between that node and a leaf. It measures how "deep" the tree is from that particular node downward, helping in evaluating the complexity or the required depth-first traversal from that node.
Why other options are wrong
A. The number of edges between the root and the deepest leaf.
This is the definition of the height of the tree, not of an individual node. The tree’s height is measured from the root to the deepest leaf, but for a node, we only consider the paths starting from that specific node.
B. The number of edges between the node and the closest leaf.
This would define the minimum depth from a node to a leaf, but it doesn't represent the height. Height always refers to the longest path to a descendant leaf.
D. The number of edges between the node and the tree root.
This defines the depth of a node, not its height. Depth refers to how far a node is from the root, while height measures from the node to its deepest descendant.
The time complexity of a tree traversal is
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O(n)
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O(log n)
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O(n log n)
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O(n^2)
Explanation
Correct Answer
A. O(n)
Explanation
The time complexity of a tree traversal (whether it is in-order, pre-order, post-order, or level-order) is typically O(n), where n is the number of nodes in the tree. This is because each node is visited once, and the amount of work done per node is constant. Therefore, the overall complexity is linear in relation to the number of nodes.
Why other options are wrong
B. O(log n)
This is incorrect because O(log n) typically applies to algorithms involving operations on balanced binary search trees like searching or insertion, not to full tree traversal, where every node must be visited.
C. O(n log n)
This is incorrect because O(n log n) time complexity applies to algorithms like sorting, not tree traversals. A tree traversal requires visiting each node once, so the time complexity is linear, not logarithmic combined with linear.
D. O(n^2)
This is incorrect because O(n^2) would suggest that the algorithm's time complexity increases quadratically with the number of nodes, which is not the case for tree traversal. Each node is visited only once, so the time complexity is linear.
A tree data structure establishes a _______ relationship between the root node and its subsequent child nodes.
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Hierarchical
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Internal
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Complete
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Inheritance
Explanation
Correct Answer
A. Hierarchical
Explanation
A tree data structure represents a hierarchical relationship, where the root node is at the top, and each node (except for the root) has a parent node. This relationship resembles a tree structure, where parent-child relationships dictate the flow of data or control, forming a hierarchy of nodes.
Why other options are wrong
B. Internal
This is incorrect because "internal" does not describe the relationship between the root and its child nodes. It might be used in a different context, but it does not specifically refer to the structure of the tree itself.
C. Complete
This is incorrect because "complete" refers to a specific type of binary tree where all levels are filled, except possibly the last, which is filled from left to right. It does not describe the general relationship between the root and child nodes.
D. Inheritance
This is incorrect because "inheritance" is a concept used in object-oriented programming to describe the relationship between classes, not in the context of tree data structures. It does not describe the structural relationship of nodes in a tree.
Why is it important for a hash function to achieve uniform distribution of hash values across its output range?
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To ensure that all keys are stored in the same location
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To minimize the chances of collisions and improve efficiency
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To allow for faster retrieval of data from the hash table
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To simplify the implementation of the hash function
Explanation
Correct Answer
B. To minimize the chances of collisions and improve efficiency
Explanation
A uniform distribution of hash values helps ensure that keys are spread out evenly across the hash table. This reduces the likelihood of collisions, which occur when two keys are hashed to the same location. Fewer collisions result in better performance and faster data retrieval, as fewer operations are needed to resolve hash conflicts.
Why other options are wrong
A. To ensure that all keys are stored in the same location
This is incorrect because if all keys were stored in the same location, it would defeat the purpose of a hash table. Collisions would occur frequently, and the performance of the table would degrade significantly.
C. To allow for faster retrieval of data from the hash table
While uniform distribution does improve retrieval speed by reducing collisions, the main reason for achieving uniform distribution is to minimize collisions, not directly to speed up retrieval. The reduction in collisions indirectly leads to faster data retrieval.
D. To simplify the implementation of the hash function
This is incorrect because achieving uniform distribution may actually make the hash function more complex. A simple hash function may not distribute keys evenly, which is why it's important to design a good hash function to achieve this goal.
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