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Quizzes > Programming & Software

7.9.1 & 7.6.1 Basic Data Structures Quiz

Quick, free data structures quiz with 20 questions. Instant results.

Editorial: Review CompletedCreated By: Lisa PeckUpdated Aug 25, 2025
Difficulty: Moderate
Grade: Grade 11
Study OutcomesCheat Sheet
Paper art promoting trivia quiz for Data Structures Mastery practice questions.

This 7.9.1 basic data structures quiz helps you check key ideas from units 7.6.1 and 7.9.1 with 20 short questions. See what you know, get quick feedback, and spot topics to review. For more practice, try our quiz on data structures, build foundations with a programming basics quiz, or sharpen logic in a JavaScript control structures quiz.

Which data structure is most appropriate for storing a fixed number of elements in a contiguous block of memory?
Queue
Stack
Array
Linked List
Arrays allocate memory contiguously, allowing for direct access to any element using its index. This design makes arrays ideal for storing a fixed number of elements with efficient access.
What principle does a stack data structure operate on?
First In, First Out (FIFO)
Sorted Order
Random Order
Last In, First Out (LIFO)
A stack follows the Last In, First Out (LIFO) principle, which means the most recently added element is the first one to be removed. This simple yet powerful behavior is widely used in programming for function calls and undo operations.
Which data structure allows insertion and deletion at both ends?
Array
Deque
Stack
Queue
A deque, or double-ended queue, enables insertion and removal of elements from both the front and rear ends. This versatility differentiates it from more restricted structures like stacks and queues.
What is a linked list composed of?
A hierarchy of parent and child nodes
Fixed-size elements stored in contiguous memory
Nodes that contain data and a reference to the next node
Elements arranged in a circular buffer
A linked list is composed of nodes, each storing data and a pointer to the next node. This structure supports dynamic memory allocation and facilitates efficient insertions and deletions.
Which operation is typically the most efficient in a stack?
Search
Push
Insert at Beginning
Delete at Arbitrary Position
The push operation on a stack is performed in constant time O(1) since it only involves adding an element at the top. This efficiency is an inherent advantage of using a stack for managing data in a LIFO manner.
In an array, what is the time complexity to access an element by its index?
O(n)
O(1)
O(n^2)
O(log n)
Arrays offer constant time complexity O(1) for accessing an element by its index due to their contiguous memory allocation. This direct addressing makes arrays highly efficient for random access.
Which data structure is most suitable for implementing a call stack in programming?
Array
Linked List
Queue
Stack
The stack data structure is ideal for handling function calls in programming due to its Last In, First Out (LIFO) nature. It efficiently manages active functions and their return addresses.
What characteristic distinguishes a singly linked list from a doubly linked list?
Singly linked lists use contiguous memory
Singly linked lists are always sorted
Singly linked lists have fixed sizes
Singly linked lists have nodes that reference only the next node, while doubly linked lists reference both previous and next nodes
The key difference is that singly linked lists use a single pointer to reference the next node, whereas doubly linked lists utilize two pointers for bidirectional traversal. This additional pointer in doubly linked lists allows for more flexible navigation.
What is the primary benefit of using a linked list over an array for dynamic data storage?
Linked lists allow efficient insertions and deletions without shifting elements
Linked lists use less memory than arrays
Linked lists maintain sorted order automatically
Linked lists provide faster random access
Linked lists excel in dynamic storage because they allow for quick insertions and deletions without the overhead of shifting elements as required in arrays. This makes them particularly useful when the data size frequently changes.
Which data structure is most suitable for breadth-first traversal in a tree?
Linked List
Stack
Array
Queue
Breadth-first traversal, or level-order traversal, uses a queue to process nodes in the order they are encountered. This ensures that nodes at each level are visited before moving deeper into the tree.
What is the time complexity to insert an element at the beginning of a linked list?
O(log n)
O(n)
O(1)
O(n^2)
Inserting an element at the beginning of a linked list is done in constant time O(1) because it involves updating just one or two pointers. This makes linked lists particularly efficient for operations at the head.
Which data structure is typically used to implement recursion in programming?
Tree
Queue
Array
Stack
Recursion inherently relies on a call stack to keep track of function calls and returns. This stack-based mechanism ensures that each recursive call is properly managed until the base case is met.
In a binary search tree (BST), what property must each node satisfy?
All nodes in the left subtree are greater than the node, and all nodes in the right subtree are less
All nodes in the left subtree are less than the node, and all nodes in the right subtree are greater
All nodes have equal values
Nodes are arranged in a circular pattern
A binary search tree is structured so that for any given node, the left subtree contains values that are less, and the right subtree contains values that are greater. This ordering facilitates efficient searching, insertion, and deletion operations.
What is a key advantage of using a hash table compared to a binary search tree?
Guaranteed sorted order of elements
Simpler implementation
Faster average lookup time
Less memory usage
Hash tables provide an average lookup time of O(1), making them very efficient for search operations. However, this comes at the cost of not preserving the order of elements, unlike binary search trees.
What is the result of a 'pop' operation on a stack?
It removes and returns the top element
It returns the bottom element without removing it
It adds a new element on top
It sorts the elements in the stack
The pop operation in a stack removes the element at the top and returns it, adhering to the Last In, First Out (LIFO) principle. This operation is fundamental to the stack's behavior and is performed in constant time O(1).
How does the choice between an array and a linked list affect memory usage and access time?
Arrays use contiguous memory offering faster access while linked lists use dynamic memory leading to potentially wasted space and slower access
Arrays and linked lists have identical memory usage and access patterns
Linked lists always use less memory than arrays and provide faster access
Linked lists use contiguous memory which is more efficient than arrays
Arrays allocate memory in continuous blocks, which enables rapid index-based access but requires a fixed size. Linked lists, in contrast, allow for dynamic resizing but incur extra memory overhead due to pointer storage, which can slow down access times.
Which data structure modification can be implemented to improve the search time complexity in a linked list?
Converting it into a circular linked list
Transforming it into a doubly linked list
Using a skip list technique to add additional forward pointers
Storing elements in a fixed-size array within the list
A skip list enhances a standard linked list by inserting extra forward pointers that allow skipping over multiple nodes. This modification reduces the number of comparisons needed during search operations, thereby improving efficiency on average.
When using a hash table, what is a common method to resolve collisions, and how does it work?
Binary search, which splits the table in half repeatedly
Chaining, where each bucket stores a linked list of entries that hash to the same index
Divide and conquer, by partitioning the hash table into segments
Stack-based resolution, by pushing entries onto a stack
Chaining resolves collisions in a hash table by storing all elements that hash to the same index in a linked list (or similar structure). This method maintains separate chains for entries with identical hash values, allowing for effective collision handling.
What is the time complexity for searching an element in a balanced binary search tree, and why?
O(n log n) due to recursive traversal
O(log n) because each comparison divides the search space in half
O(n) because it must check every node
O(1) because of direct access to nodes
A balanced binary search tree halves the search space with each comparison, leading to a logarithmic time complexity O(log n) for search operations. This efficiency is achieved by maintaining a balanced structure where the heights of the two subtrees differ minimally.
How can using a stack assist in the process of evaluating arithmetic expressions written in postfix notation (Reverse Polish Notation)?
Stacks allow orderly operand storage and efficient evaluation of operators by popping operands for computation
Stacks guarantee the minimal memory usage during evaluation
Stacks convert postfix expressions to infix expressions
Stacks sort the expression before evaluating it
In postfix expression evaluation, a stack is used to temporarily hold operands until an operator is encountered. When an operator appears, the required number of operands are popped from the stack, the operation is performed, and the result is pushed back, ensuring an orderly and efficient computation process.
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Study Outcomes

  1. Understand the core concepts behind arrays, lists, stacks, and queues.
  2. Apply basic algorithms to manipulate and traverse common data structures.
  3. Analyze the efficiency and complexity of different data structures.
  4. Implement data structures to solve common computational problems.
  5. Evaluate and compare the suitability of various data structures for real-world applications.

Basic Data Structures Quiz 7.6.1 & 7.9.1 Cheat Sheet

  1. Fundamental Array Operations - Arrays are like super-organised bookshelves where each book (element) has its own spot, so grabbing an item by index is lightning-fast O(1). But slipping a new volume at the front can trigger a full shelf shuffle, costing O(n). Remember these time quirks to keep your code snappy!
  2. LIFO Principle of Stacks - Stacks are your code's bouncers: the last item you push on the stack is the first one you pop off. This Last-In, First-Out magic drives function calls, undo features, and even expression evaluations. Mastering stacks helps you juggle nested operations like a pro!
  3. FIFO Principle of Queues - Queues line up like snack fans at a concert: whoever arrives first gets served first, thanks to First-In, First-Out. They're perfect for scheduling tasks, print spools, and buffering data streams. Get comfortable with enqueue and dequeue to keep processing smooth and fair!
  4. Linked List Mechanics - Linked lists are like a treasure map chain: each node points to the next, so you can insert or remove mid-journey without redrawing the entire map. No need for contiguous memory means you avoid big block searches. Dive into pointers to unlock dynamic data structures!
  5. Binary Tree Traversals - Binary trees branch out like family trees, with each node juggling up to two children. In-order, pre-order, and post-order traversals let you visit nodes in different "family reunion" orders. Practice these to unlock sorted outputs, tree cloning, and more adventures!
  6. Hash Table Essentials - Hash tables are like magical dictionaries: you feed in a key, and poof - you instantly get the value in average O(1) time. Collisions can clash keys, but chaining or open addressing keeps the party under control. Get hashing, and you'll be a lookup wizard!
  7. Heaps and Priority Queues - Heaps behave like tournament brackets: in a max-heap the champion (largest element) sits at the top, while a min-heap puts the underdog (smallest) in pole position. This structure fuels priority queues where the VIP items get served first. Learn to heapify, and you'll ace scheduling tasks!
  8. Graph Fundamentals - Graphs are social networks for data, with vertices as people and edges as connections. Represent them via adjacency lists or matrices depending on your memory vs. speed needs. Explore graph basics, and you'll map friendships, routes, and relationships with flair!
  9. DFS and BFS Traversals - Depth-First Search (DFS) dives deep down one path before backtracking, like exploring a dungeon corridor fully before turning back. Breadth-First Search (BFS) fans out layer by layer, like ripples in a pond. Master both to solve mazes, shortest paths, and connectivity puzzles!
  10. Choosing the Right Data Structure - Picking the perfect data structure is like choosing the right tool: you wouldn't use a hammer to tighten a screw! Balance time complexity, memory use, and ease-of-use to solve your challenge. Nailing this decision makes you a coding superstar!
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