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Quizzes > Programming & Software

Coding Bootcamp Assessment Test: Practice Quiz

Quick, free coding aptitude test. Instant results and answer explanations.

Editorial: Review CompletedCreated By: Gina BrownUpdated Aug 28, 2025
Difficulty: Moderate
Questions: 20
Learning OutcomesStudy Material
Colorful paper art depicting elements related to a Coding Bootcamp Aptitude Test quiz

This Coding Bootcamp Assessment Test practice quiz helps you gauge your readiness and spot gaps before training. Answer 15 quick multiple-choice questions with instant feedback, then build fundamentals with our programming basics quiz and keep sharpening with quiz prep exercises; if you are still exploring the field, try the is coding for me quiz.

What is the time complexity of accessing an element in an array by index?
O(n)
O(n log 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
Tree
Stack
Queue
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?
lambda
def
func
function
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?
do-while
while
for
foreach
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?
1
0
5
4
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?
Quicksort
Bubble sort
Selection sort
Insertion sort
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?
Queue
Linked list
Stack
Hash table
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(log n)
O(n)
O(1)
O(n log n)
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?
Automatically sorted keys
No risk of collisions
Average O(1) lookup time
Guaranteed FIFO ordering
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'?
A helper function
The stopping condition that prevents infinite calls
The initial call to the function
The recursive call itself
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?
Missing semicolon after print
print() cannot access array elements
Loop should use 'while' instead of 'for'
It should start from index 0, not 1
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?
Greedy algorithms
Brute force
Dynamic programming
Divide and conquer
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?
4
5
2
3
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
Array
Doubly linked list
Stack
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 log n)
O(n^2)
O(n)
O(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^2)
O(n log n)
O(log n)
O(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
Branch and bound
Greedy method
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(1)
O(n log n)
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?
Change p to a float pointer
Remove the printf statement
Allocate memory for p before assignment
Use a double pointer
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(n)
O(n log n)
O(n^2)
O(2^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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
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