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Quizzes > Quizzes for Business > Technology

Coding Bootcamp Aptitude Test Practice Quiz

Sharpen Your Coding Aptitude Skills in Minutes

Difficulty: Moderate
Questions: 20
Learning OutcomesStudy Material
Colorful paper art depicting elements related to a Coding Bootcamp Aptitude Test quiz

Ready to tackle the Coding Bootcamp Aptitude Test? This practice quiz challenges beginners and aspiring developers with multiple-choice coding questions that mirror real bootcamp assessments. Participants will sharpen logic, problem-solving, and time-management skills while getting instant feedback. Anyone preparing for a bootcamp entry exam or seeking a self-assessment can benefit, and it's fully customizable in our editor. Also explore Bootcamp Knowledge Assessment Quiz or test broader skills with the Quantitative Aptitude Test, and browse all quizzes to find the perfect fit.

What is the time complexity of accessing an element in an array by index?
O(n log n)
O(n)
O(1)
O(log n)
Accessing an array element by its index is a constant-time operation because it directly computes the memory address. Therefore, the time complexity is O(1).
Which data structure uses a first-in, first-out (FIFO) principle?
Graph
Stack
Queue
Tree
A queue processes elements in the order they arrive, removing from the front and adding at the back. This behavior is known as first-in, first-out (FIFO).
Which keyword is used to define a function in Python?
function
func
lambda
def
The 'def' keyword in Python is used to introduce a function definition followed by its name and parameters. Other keywords like 'lambda' create anonymous functions.
Which loop in most programming languages executes its body at least once before checking its condition?
for
do-while
foreach
while
A do-while loop runs its body first and evaluates the loop condition afterward, guaranteeing at least one execution. Other loops check their conditions before entering the body.
If an array has 5 elements, what is the index of the last element in a zero-based index system?
0
4
5
1
Zero-based indexing starts counting indices at 0. For 5 elements, indices range from 0 to 4, making 4 the last valid index.
Which sorting algorithm has an average time complexity of O(n log n) and uses a divide-and-conquer approach?
Selection sort
Bubble sort
Insertion sort
Quicksort
Quicksort divides the array into subarrays around a pivot and recursively sorts them, yielding an average time complexity of O(n log n). Other simple sorts run in O(n^2).
Which data structure is the best choice for Last-In, First-Out (LIFO) operations?
Linked list
Hash table
Queue
Stack
A stack follows a Last-In, First-Out (LIFO) ordering, where the most recently added element is removed first. Queues use FIFO instead.
What is the space complexity of the merge sort algorithm?
O(n log n)
O(log n)
O(n)
O(1)
Merge sort requires additional arrays to merge subarrays, leading to a linear extra space requirement of O(n). In-place sorts use less memory.
What is the primary advantage of using a hash table?
Guaranteed FIFO ordering
Average O(1) lookup time
Automatically sorted keys
No risk of collisions
Hash tables map keys to indices via a hash function, allowing average-case constant time lookups, inserts, and deletions. Collisions can still occur but are handled by various strategies.
In recursion, what is referred to as the 'base case'?
The initial call to the function
The stopping condition that prevents infinite calls
The recursive call itself
A helper function
The base case is a condition under which the recursive function returns a result without making further recursive calls. It prevents infinite recursion.
Consider the pseudocode snippet: for i from 1 to n: print(arr[i]) What is the error in this code if 'arr' is a zero-based array?
It should start from index 0, not 1
Loop should use 'while' instead of 'for'
Missing semicolon after print
print() cannot access array elements
Zero-based arrays start at index 0, so accessing arr[1] skips the first element and eventually tries arr[n], which is out of bounds. The loop should start at 0 and go to n-1.
Which algorithmic paradigm solves problems by breaking them into overlapping subproblems and storing their solutions?
Dynamic programming
Greedy algorithms
Divide and conquer
Brute force
Dynamic programming identifies overlapping subproblems and caches their solutions to avoid redundant work. Divide and conquer splits without necessarily reusing results.
If an aptitude test has 60 minutes to solve 20 questions, how many minutes should you allocate per question to maintain a consistent pace?
3
5
4
2
Dividing 60 minutes by 20 questions yields 3 minutes per question, helping to manage time evenly across the test. Adjustments can be made if questions vary in difficulty.
Which data structure uses nodes connected by pointers and allows efficient insertions and deletions at both ends?
Binary search tree
Stack
Doubly linked list
Array
A doubly linked list maintains pointers to both the next and previous nodes, enabling O(1) insertions and deletions at either end. Arrays require shifting elements.
What is the worst-case time complexity of quicksort?
O(n)
O(log n)
O(n^2)
O(n log n)
In the worst case, when pivots divide the array poorly (e.g., always smallest or largest), quicksort degrades to O(n^2). Average case remains O(n log n).
Given the recurrence relation T(n) = 2T(n/2) + n, what is the time complexity T(n)?
O(n)
O(n log n)
O(n^2)
O(log n)
By the Master Theorem, a=2, b=2, and f(n)=n, which matches case 2 giving T(n)=O(n log n). This is typical for merge sort.
Which technique in dynamic programming involves storing results of expensive function calls and reusing them when the same inputs occur again?
Divide and conquer
Memoization
Greedy method
Branch and bound
Memoization caches function outputs keyed by their inputs to avoid redundant calculations, speeding up algorithms with overlapping subproblems.
What is the amortized time complexity of inserting an element into a dynamic array (e.g., ArrayList) over a sequence of operations?
O(n log n)
O(1)
O(n)
O(log n)
Although occasional resizes cost O(n), most insertions are O(1). Amortized over many operations, the average insertion costs O(1).
Examine the following C code snippet causing a segmentation fault: int *p; *p = 5; printf("%d", *p); What is the most direct fix to avoid the fault?
Allocate memory for p before assignment
Change p to a float pointer
Use a double pointer
Remove the printf statement
Pointer p is uninitialized and doesn't point to valid memory. Allocating memory (e.g., via malloc) or assigning it to the address of a valid int fixes the segmentation fault.
What is the time complexity of the naive recursive Fibonacci function without memoization?
O(2^n)
O(n^2)
O(n log n)
O(n)
The naive recursive Fibonacci makes two calls for each non-base case, forming a binary recursion tree with exponential growth, resulting in O(2^n) time complexity.
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Learning Outcomes

  1. Identify core coding concepts commonly tested in bootcamp assessments
  2. Apply problem-solving strategies to algorithmic challenges
  3. Analyse data structures for efficiency in coding scenarios
  4. Master time-management skills required for aptitude exams
  5. Evaluate code snippets to detect and correct errors
  6. Demonstrate readiness with simulated test questions

Cheat Sheet

  1. Master Fundamental Programming Constructs - Get cozy with loops, conditionals, and basic syntax - they're like the alphabet of coding that you'll use everywhere. Try writing short snippets daily to make these patterns second nature. Dive into syntax basics
  2. Understand Data Structures and Algorithms - Think of data structures as your toolkit and algorithms as the recipes that use them. When you know how arrays, stacks, and sorting techniques really work, solving problems becomes a breeze. Explore DS & Algo essentials
  3. Enhance Logical Reasoning and Pattern Recognition - Puzzle fans, this one's for you! Tackling brain teasers and riddles will sharpen your mind for spotting coding patterns and edge cases. Sharpen logic skills
  4. Develop Efficient Debugging Techniques - Debugging isn't just about fixing bugs - it's an art form. Learn to read error messages like a detective and use tools to step through code for smoother, faster fixes. Debug like a pro
  5. Practice Time Management with Timed Drills - Ready, set, code! Simulate real test pressure with timers to build speed and accuracy under the clock. Reviewing completed drills helps you spot time sinks and streamline your approach. Try timed challenges
  6. Get Comfortable with Your Computer - Faster fingers and clever shortcuts can shave minutes off your solutions. Customize your workspace, learn keybindings, and watch your coding sessions become a breeze. Boost your coding setup
  7. Complete Prework and Practice Problems - If you've got gear, use it! Dive into any provided prework or problem sets to build familiarity and spot weak spots before they catch you by surprise. Tackle prep exercises
  8. Prepare for Cognitive Aptitude Tests - This is the mental fitness portion: CCAT, any brain-builder will do. Regularly solve logic and math puzzles to train your critical thinking and speed. CCAT prep guide
  9. Conquer Impostor Syndrome - Even the pros had a shaky start! Celebrate small wins, journal your progress, and remind yourself that every coder was once a beginner. Beat self-doubt
  10. Seek Feedback and Continuous Improvement - Code reviews, pair programming, or chat with a friend - fresh eyes spot hidden gems and glitches in your work. Embrace feedback loops to level up faster. Join the feedback loop
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