Choosing data structures for interview problems in Data Structures Theory - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to choose the best data structure for fast lookup.

Data Structures Theory

best_structure = [1]

Drag options to blanks, or click blank then click option'

Ahash table

Blinked list

Carray

Dstack

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a linked list which requires linear search.

A hash table provides fast lookup by key, usually in constant time.

2fill in blank

medium

Complete the code to select a data structure that maintains order and allows fast insertion.

Data Structures Theory

chosen_structure = [1]

Drag options to blanks, or click blank then click option'

Alinked list

Bhash set

Carray

Dqueue

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing array which requires shifting elements on insertion.

A linked list maintains order and allows fast insertion and deletion anywhere.

3fill in blank

hard

Fix the error in choosing the data structure for last-in-first-out access.

Data Structures Theory

data_structure = [1]

Drag options to blanks, or click blank then click option'

Abinary tree

Bstack

Chash map

Dqueue

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing queue which is first-in-first-out (FIFO).

A stack follows last-in-first-out (LIFO) order, suitable for this access pattern.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters keys starting with 'a'.

Data Structures Theory

{k: v for k, v in data.items() if k.[1]('a') and v [2] 10}

Drag options to blanks, or click blank then click option'

Astartswith

B>

C<

Dendswith

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'endswith' instead of 'startswith'.

Using '<' instead of '>' for filtering values.

Use startswith to check keys starting with 'a' and > to filter values greater than 10.

5fill in blank

hard

Fill all three blanks to create a set comprehension selecting even numbers greater than 5.

Data Structures Theory

{num for num in numbers if num [1] 5 and num [2] 2 == [3]

Drag options to blanks, or click blank then click option'

A>

B%

C0

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for comparison.

Checking modulo equals 1 instead of 0.

Filter numbers greater than 5 and check if they are even by using modulo 2 equals 0.

Practice

1. Which data structure is best suited for implementing a phone book where you need to quickly find a person's phone number by their name?

easy

A. Linked List

B. Queue

C. Stack

D. Hash Table

Solution

1. Step 1: Understand the problem requirement

   The problem requires fast lookup of phone numbers by name, which means quick access based on a key.

2. Step 2: Choose data structure with fast key-based access

   Hash Tables provide average constant time O(1) lookup by key, making them ideal for this use case.

3. Final Answer:

   Hash Table -> Option D

4. Quick Check:

   Fast key lookup = Hash Table [OK]

Hint: Use hash tables for fast key-value lookups [OK]

Common Mistakes:

• Choosing linked list which has slow search
• Using stack or queue which are not for key-based lookup

2. Which of the following is the correct syntax to declare an empty stack using a list in Python?

easy

A. stack = {}

B. stack = []

C. stack = ()

D. stack = set()

Solution

1. Step 1: Recall Python data types for stack implementation

   Stacks are commonly implemented using lists in Python because lists support append() and pop() methods.

2. Step 2: Identify correct syntax for empty list

   An empty list is declared with square brackets []. Curly braces {} create a dictionary, () a tuple, and set() a set.

3. Final Answer:

   stack = [] -> Option B

4. Quick Check:

   Empty list syntax = stack = [] [OK]

Hint: Use [] to create an empty list for stack in Python [OK]

Common Mistakes:

• Using {} which creates a dictionary, not a list
• Using () which creates a tuple, not a list

3. Consider the following Python code snippet:

queue = []
queue.append(10)
queue.append(20)
queue.pop(0)
print(queue)

What will be the output?

medium

A. [20]

B. Error

C. [10]

D. [10, 20]

Solution

1. Step 1: Trace the queue operations

   Initially queue is empty []. After append(10), queue = [10]. After append(20), queue = [10, 20].

2. Step 2: Understand pop(0) effect

   pop(0) removes the first element (10), so queue becomes [20].

3. Final Answer:

   [20] -> Option A

4. Quick Check:

   pop(0) removes first element = [20] [OK]

Hint: pop(0) removes first item in list acting as queue [OK]

Common Mistakes:

• Thinking pop() removes last element by default
• Expecting no change after pop(0)
• Confusing list with stack behavior

4. You wrote this code to implement a stack using a list in Python:

stack = []
stack.push(5)
stack.push(10)
print(stack.pop())

What is the error in this code?

medium

A. Stack cannot be implemented with list

B. 'pop' method does not exist for list

C. 'push' method does not exist for list

D. Syntax error in print statement

Solution

1. Step 1: Check list methods for stack operations

   Python lists use append() to add elements, not push().

2. Step 2: Identify the error

   Calling stack.push(5) causes AttributeError because 'push' is not a list method.

3. Final Answer:

   'push' method does not exist for list -> Option C

4. Quick Check:

   Use append() for stack push in list [OK]

Hint: Use append() to push on list, not push() [OK]

Common Mistakes:

• Assuming push() is a list method
• Thinking pop() is missing
• Believing stack can't be made with list

5. You need to design a system that keeps track of the top 3 highest scores in a game, updating efficiently as new scores arrive. Which data structure is the best choice?

hard

A. Min-Heap of size 3

B. Max-Heap of all scores

C. Linked List sorted ascending

D. Queue storing last 3 scores

Solution

1. Step 1: Understand the requirement for top 3 scores

   We want to keep only the highest 3 scores updated efficiently as new scores come in.

2. Step 2: Choose data structure for efficient top-k tracking

   A min-heap of size 3 keeps the smallest of the top scores at the root. When a new score arrives, compare with root; if higher, replace root and heapify. This keeps top 3 efficiently.

3. Final Answer:

   Min-Heap of size 3 -> Option A

4. Quick Check:

   Top-k tracking = Min-Heap size k [OK]

Hint: Use min-heap of size k to track top k items efficiently [OK]

Common Mistakes:

• Using max-heap of all scores wastes memory
• Using linked list is slow for updates
• Queue stores last scores, not top scores