20+ Best Code Signal Questions with Answers for Practice

Coding interview platforms combine strict proctoring and anti-cheating checks with timed algorithm problems, so mastering common patterns in arrays, strings, trees, graphs, and dynamic programming matters as much as knowing how the platform grades your code. How do you practice the exact code signal questions you will see and cut down your time without guessing? This article breaks down frequent challenge questions, offers practical strategies for data structures and algorithm tasks, and shows test-taking techniques so you can confidently walk into a code signal assessment knowing you’ve already mastered the exact types of questions you’ll face, with the skills to solve them quickly and accurately.To help you reach that level, Interview Coder provides a focused interview practice assistant that simulates real test problems, enables you to build fast, correct solutions, and keeps the work honest by training skills rather than shortcuts.

20+ Best Code Signal Questions for Practice

1. Sliding Triplet Sum (Array Manipulation)

Problem (CodeSignal style):

• Given an array a, return an array b of the same length where for each i: b[i] = a[i - 1] + a[i] + a[i + 1] If an index is out of bounds, use 0 for that term.

Input/Output example:

• For a = [4, 0, 1, -2, 3], solution(a) = [4, 5, -1, 2, 1].

Python solution (adaptable to other languages):

def solution(a):
n = len(a)
b = [0] * n

for i in range(n):
s = a[i]

if i > 0:
s += a[i - 1]

if i < n - 1:
s += a[i + 1]

b[i] = s

return b

Reasoning, step by step:

• Initialize b with zeros to hold results for every index.
• For each index i, start with a[i]. Add a[i - 1] only if i > 0. Add a[i + 1] only if i < n - 1
• Using bounds checks avoids IndexError and implements the rule that missing neighbors count as zero.
• Common pitfall: Trying to access neighbors without checking bounds or attempting to modify a while reading neighbors in a way that corrupts values. This approach reads from a only and writes to b, which prevents interference.
• Alternative: Pad the array with zeros on both ends and compute sums without explicit checks. That trades a small amount of memory for simpler indexing.

Skills tested:

• Array traversal, boundary handling, fundamental indexing, and careful edge case handling.

Complexity:

• Time: O(n) where n = len(a). Each element is visited once
• Space: O(n) for the output array b (plus O(1) auxiliary)

2. Vowel Consonant Pattern Match (String Pattern Matching)

Problem (CodeSignal style):

• Given pattern (string of '0' and '1') and source (lowercase letters), count substrings of source that match pattern length where:
  • '0' in pattern means vowel in substring
  • 1' means consonant in substringVowels set = {a, e, i, o, u, y}.

Input/Output examples:

• pattern = "010", source = "amazing" → solution(pattern, source) = 2
• pattern = "100", source = "codesignal" → solution(pattern, source) = 0

Python solution:

VOWELS = set('aeiouy')

def matches_at(pattern, source, start):
"""Check if pattern matches source at given start index."""
for k, ch in enumerate(pattern):
c = source[start + k]

if pattern[k] == '0':
if c not in VOWELS:
return False
else:
if c in VOWELS:
return False

return True

def solution(pattern, source):
"""Count occurrences of pattern in source."""
lp = len(pattern)
ls = len(source)

if lp > ls:
return 0

count = 0
for start in range(ls - lp + 1):
if matches_at(pattern, source, start):
count += 1

return count

Reasoning, step by step:

• Precompute the vowel set for O(1) membership checks.
• For every possible starting index where a substring of length len(pattern) fits, test each position:
  • If pattern char is '0', require a vowel, else require consonant
• Return the count of successful matches.
• Common pitfalls: Treating y as a consonant, mixing uppercase letters, or not checking when the pattern is longer than source.
• Alternative: Convert source to a bitmask array where vowel=0, consonant=1, and compare integer patterns or use rolling checks. That can speed up significant inputs, but is unnecessary under the given constraints.

Skills tested:

• String traversal, pattern matching, use of helper functions, edge case handling.

Complexity:

• Time: O(lp * (ls - lp + 1)) worst case, which is O(lp * ls). Given constraints up to 103 each, this is fine.
• Space: O(1) extra space besides input and trivial counters.

3. Tetris Drop and Row Completion (Two-Dimensional Array Traversal)

Problem (CodeSignal style):

• Given field matrix height × width and figure matrix 3 × 3 of 0s and 1s, choose a column where the 3×3 figure top left fits inside the field, drop it straight down until it collides with ground or occupied cell, then check whether at least one whole row (all ones) is formed after placement.
• Return any valid column index or -1 if none.

Input/Output examples:

• field = [[0,0,0],[0,0,0],[0,0,0],[1,0,0],[1,1,0]]; figure = [[0,0,1],[0,1,1],[0,0,1]] → solution = 0
• field = [[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0],[1,1,0,1,0],[1,0,1,0,1]]; figure = [[1,1,1],[1,0,1],[1,0,1]] → solution = 2
• field = [[0,0,0,0],[0,0,0,0],[0,0,0,0],[1,0,0,1],[1,1,0,1]]; figure = [[1,1,0],[1,0,0],[1,0,0]] → solution = -1
  

Python solution:

Reasoning, step by step:

• Iterate every valid column where a 3×3 figure can be placed.
• Simulate vertical drop:
  • Start with the figure at row 0 and attempt to move it down while the figure stays entirely inside the field and does not overlap a 1 in the field.
  • Collisions are detected by checking any pair of overlapping 1s between the figure and the field at the candidate's next position.
• After the piece stops, check the f rows the figure covers. For each of those rows, compute whether every cell becomes one either because the field already had one there or because the figure contributes one at that column.
• If any such row is fully 1, return the column.
• Common pitfalls: Forgetting that the figure must remain entirely inside the field during placement, or checking rows outside valid bounds. Another mistake is updating a field in-place and then reusing it across columns without resetting. The presented solution reads the field and figure and does not permanently mutate the field, which keeps each trial independent.
• Alternative approach: Produce a temporary copy of the few affected rows and test them instead of checking on the fly. That can make logic more straightforward when adjusting for in-place mutation.

Skills tested:

• 2D array traversal, collision detection, simulation, careful indexing, spatial reasoning.

Complexity:

• Time: O((w - f + 1) * h * f * f) in worst case. For f fixed at 3 this is effectively O(w * h).
• Space: O(1) extra space beyond inputs, aside from constant loop variables.

4. Pair Sums That Are Powers of Two (Lookup Table)

Problem (CodeSignal style):

• Given an array of unique integer numbers, count pairs (i, j) with i ≤ j such that numbers[i] + numbers[j] equals a power of two (1, 2, 4, 8, ...).

Input/Output examples:

• numbers = [1, -1, 2, 3] → solution(numbers) = 5
• numbers = [2] → solution(numbers) = 1
• numbers = [-2, -1, 0, 1, 2] → solution(numbers) = 5

Guaranteed constraints:

• 1 ≤ n ≤ 10^5, numbers[i] in [-10^6, 10^6]

Python solution:

from collections import defaultdict

def solution(numbers):
counts = defaultdict(int)
answer = 0

# Precompute powers of two (up to 2^21, which covers numbers up to ~2e6)
powers = [1 << k for k in range(22)]

for x in numbers:
# For each power, look for complement already seen
for p in powers:
need = p - x
answer += counts.get(need, 0)
counts[x] += 1

return answer


Reasoning, step by step:

• Use a dictionary to count how many times a value has appeared while scanning left to right.
• For the current element x, for each relevant power of two p, compute need = p - x. If a need was seen before, each occurrence forms a valid pair with the current x and increases the count by counts[need].
• After checking all powers, record x in counts so later elements can pair with it.
• The range of powers stops at 2^21 since numbers are bounded by ±10^6; the maximum sum magnitude that matters is within that range.
• Avoid double counting by always pairing the current element with previously seen elements only and by allowing the pair (i, j) with i == j when the same value was seen earlier (counts include prior occurrences).
• Common pitfalls: iterating unnecessary powers of two beyond the sum bounds or using a set instead of counts and losing multiplicity information.
• Alternative: sort the numbers and use two pointers for each power target. That can work, but is more complex and not faster than the hash approach for these bounds.

Skills tested:

• Hash table usage, combinatorics with counts, understanding numeric constraints, and careful off-by-one logic.

Complexity:

• Time: O(n * P) where P is number of powers considered (here P = 22), effectively O(n).
• Space: O(n) for the counts dictionary to store seen values.

Easy Questions

• String Manipulation: Reverse a string, check for anagrams, remove duplicates.
• Arrays & Hashing: Find the missing number, two sum problem, majority element.
• Basic Math Problems: Check if a number is prime, find the greatest common divisor (GCD).
• Stack & Queue: Valid parentheses, implement a queue using stacks.

Medium Questions

• Sorting & Searching: Merge intervals, binary search, quicksort.
• Dynamic Programming: Fibonacci series, longest common subsequence, subset sum problem.
• Graph Traversal: Rotten oranges (BFS), number of islands (DFS), shortest path in an unweighted graph.
• Linked List: Detect a cycle in a linked list, reverse a linked list.
• Tree Algorithms: Binary tree level order traversal, lowest common ancestor in a BST.

Hard Questions

• Advanced Graph Algorithms: Dijkstra’s shortest path, minimum spanning tree (Prim’s/Kruskal’s Algorithm).
• Dynamic Programming Challenges: Edit distance problem, longest increasing subsequence, coin change problem.
• Complex String Algorithms: KMP pattern matching, Rabin-Karp algorithm for substring search.
• Hard Data Structures: Implement a trie, LRU cache, and segment tree for range queries.
• Mathematical Challenges: N-queens problem, Sudoku solver, largest rectangle in a histogram.

Sample Questions from Visa CodeSignal Assessment

• Reverse a String: Given a string, return it in reverse order.
• Two Sum Problem: Find two numbers in an array that add up to a target value.
• Longest Substring Without Repeating Characters: Identify the most extended unique character sequence in a given string.
• Merge Intervals: Given overlapping intervals, merge them into a single continuous interval.
• Find the Missing Number: Given an array of numbers from 1 to N with one missing, determine the missing number.
• Binary Tree Level Order Traversal: Traverse a binary tree level by level.
• Find the Shortest Path in a Graph: Implement Dijkstra’s algorithm.
• Valid Parentheses: Check if a given string with parentheses is valid.
• Rotten Oranges Problem: Simulate the spread of rot in a matrix using BFS.
• Subset Sum Problem: Determine if a subset exists with a given sum.
• Sudoku Solver: Solve a partially filled Sudoku grid.
• LRU Cache Implementation: Implement an LRU caching system.
• Edit Distance Problem: Find the minimum number of operations to convert one string to another.
• Word Ladder Problem: Transform one word to another using the shortest transformation sequence.
• Trapping Rain Water: Compute the amount of water trapped between bars in an elevation map.

Related Reading

• How To Cheat On Codesignal
• Coderpad Interview
• Coderpad Interview Questions
• Does Codesignal Record Screen

How to Crack Code Signal Assessments

1. Know the Assignment: What the Assessment Asks For

You will face a coding assessment that asks you to build a simple, realistic system that models people, money, accounts, orders, or similar. Levels unlock one by one, and each next level will reuse and extend what you already built.

• Which entities change state?
• Which operations must be invoked by which actor?
• Which behaviors will the test suite check first?

Sketch the minimal domain model on paper or in comments before typing code. Keep the Code Signal Questions focus in mind: test cases drive success, not clever tricks.

Quick tactics:

• Scan all four level descriptions before implementing anything. Note required methods and visible side effects.
• Write down the public API the grader will call: class names, method names, and signatures.
• Identify core entities and the state each must own. Decide what must be persisted across calls and what can be computed on the fly.
• Use the platform run tool often so you learn how the test harness reacts to partial work.

2. Be Classy: Use Objects to Organize Behavior and State

Model real life as objects. Represent an Employee, Manager, Account, or Transaction as a class, not as a bag of loose functions. Put each operation in the class that acts. If a manager pays someone, make pay_employee a Manager method instead of get_paid on Employee. Use small classes that each have one responsibility and compose them.Practical patterns:

• Create a Transaction or Payment object to hold metadata instead of scattering fields across methods.
• Favor composition over deep inheritance. For example, an Employee may have an Account object rather than inherit account behavior.
• Keep constructors concise: Set initial state, validation, and defaults.
• Name methods clearly to match what the tests expect. The grader often calls methods directly and inspects attributes.
• In Code Signal Questions, file space is limited. Put classes upfront, helper functions below, and tests or stubs at the bottom if you need them temporarily.

3. Manage State: Modularize and Separate Concerns

The platform evaluates how your objects move from one state to the next. Every action that changes the system should update the proper object and record the event. Avoid stuffing unrelated responsibilities into one function. Break actions into atomic steps: validate, mutate, record, then return.

Concrete steps to keep the state sane:

• For each mutating action, split it into separate functions: one to perform the change and one to record the change in history. For example, withdraw calls _update_balance and _record_transaction.
• Keep histories and totals as explicit attributes, like transactions list or total_wages_received on Employee. Tests will often verify those.
• Make the state deterministic. Do not use random IDs or timestamps unless you can control them. Use simple incremental IDs when needed.
• Validate inputs early and return clear errors or booleans that tests can assert.
• Avoid global mutable state; when unavoidable, encapsulate it in a single manager class so you can reason about lifecycle and resets.

4. Slow Is Smooth, Smooth Is Fast

Time pressure pushes candidates to hack code instead of building something extensible. Plan deliberately. Spend a minute to outline what must work in the next level and which parts you can stub. A short design pause prevents lengthy rewrites later.A simple workflow to follow:

• Level start: Read the requirements and list the three features that must pass tests first.
• Sketch classes, methods, and which attributes carry state across calls. Mark the optional features to add later.
• Implement the smallest path that passes the earliest tests. Run the test harness often to avoid surprises.
• If you fall behind, stop typing and map the next 10 minutes. Ask yourself which failing tests would unlock the next level.
• Use prints or small assertions to trace the state during runs. In Code Signal Questions, the grader shows failing tests, so iterate against those.

5. Fret Not: Correctness and Tests Matter More Than Micro Optimization

Automated scoring for these coding interview platforms checks behavior and state, not asymptotic elegance. Use built-in sorting, straightforward loops, and clear logic. You are rarely penalized for O n log n solutions when the code reads well and matches tests. Keep your code readable and error-free.What to prioritize under proctoring and grading:

• Make your solution robust against the grader's input. Handle the edge cases the prompt mentions.
• Avoid external network calls, file IO, or complex system calls. Those can fail under the runtime sandbox and trigger proctoring flags.
• Do not attempt shortcuts that look like copying from elsewhere. Platforms include plagiarism detection and proctoring logs. Own your code.
• If you cannot finish a level, ensure the parts you do implement are clean and correctly update the state. Partial but correct behavior often passes early screening.
• Use clear names and small helper functions. The grader tests act like unit tests, and they prefer predictable outputs over clever one-liners.

Questions to keep you honest while coding:

• Which two behaviors must be valid for the next level to unlock?
• Which attributes must persist between method calls for the grader to accept your solution?

Related Reading

• How Does Hackerrank Detect Cheating
• Hackerrank Interview Questions

Nail Coding Interviews with Interview Coder's Undetectable Coding Assistant − Get Your Dream Job Today

• Describe an AI assistant that guides live practice sessions, explains solutions step by step, and runs test cases in your local editor.
• Highlight features that improve performance on CodeSignal Questions and online coding assessments: automated feedback on time and space complexity, tailored problem sets, error diagnosis, and progress analytics.
• Show how the tool supports mock timed tests and pair programming practice to build confidence before real interviews.

CodeSignal Questions: 8 Week Practical Study Plan

• Week 1 to 2: Core patterns for arrays and strings, two-pointer techniques, sliding window practice with timed drills.
• Week 3 to 4: Recursion, stacks, queues, and hash map use cases, with small projects that enforce writing test cases.
• Week 5 to 6: Trees and graphs, BFS and DFS templates, shortest path problems, practice on medium difficulty CodeSignal Questions under time limits.
• Week 7: Dynamic programming patterns and memoization with clear template solutions and space reduction strategies.
• Week 8: Three full-length mock assessments, post-test reviews, and interview communication drills that simulate whiteboard explanations.
• Quick tips: Parse the prompt, write minimal working code, run sample tests, then optimize complexity while narrating your approach.

Interview Integrity Playbook: How Companies and Platforms Reduce Cheating

• Use randomized problem banks and varied test cases so cached solutions lose value.
• Combine automated scoring with live technical interviews and take-home assignments that require design trade-offs.
• Apply behavior analytics to flag anomalies, and require recorded coding sessions or live pairing for high-stakes roles.
• Design assessments that test applied knowledge: code review tasks, system design mini projects, and debugging challenges that need reasoning beyond direct copy-paste.
• Encourage clear candidate instructions and an honor code while balancing privacy and fairness.

Related Reading

• Hackerrank Proctoring
• Coderpad Cheating
• Hackerrank Cheating