2026 Coding Test Preparation Complete Guide - Algorithms, Data Structures & Language Selection

Introduction: Why Coding Tests Are Unavoidable

As of 2026, coding tests have become a virtually mandatory gateway for employment in the IT industry. Not only major Korean IT companies such as Samsung Electronics, Kakao, Naver, and LINE, but also unicorn startups like Coupang, Baedal Minjok (Woowa Brothers), and Toss require coding tests as part of their hiring process. According to a 2025 survey by the Korea Economic Research Institute, approximately 92% of domestic IT companies conduct coding tests for new hires, and about 65% also administer coding tests alongside technical interviews for experienced hires.

There is a clear reason behind the growing importance of coding tests. Simply knowing the syntax of a programming language is an entirely different competency from possessing the algorithmic thinking needed to solve given problems efficiently. Through coding tests, companies comprehensively evaluate candidates' logical thinking, problem decomposition skills, optimization instincts, and practical ability to write working code under time constraints.

Non-CS majors can absolutely rise to the challenge. According to 2025 Programmers statistics, approximately 30% of coding test passers come from non-CS backgrounds, and with a systematic study plan and consistent practice, one's major is not a decisive factor. In this article, we will thoroughly cover how to systematically prepare for coding tests in 2026 -- from algorithms, data structures, and language selection to learning platforms and practical strategies.

1. Types of Coding Tests and Question Trends

1.1 Company-Specific Coding Test Types

Each company has its own unique coding test style, and understanding these is the first step toward efficient preparation.

Company	Test Format	Questions / Time	Question Characteristics	Difficulty
Samsung SW Aptitude Test	Offline (company PC)	2 problems / 3 hours	Implementation, simulation, BFS/DFS focused	Medium-High
Kakao Blind Recruitment	Online (Programmers)	7 problems / 5 hours	Balanced mix of strings, implementation, DP, graphs	Medium-Very High
Naver/LINE	Online	4-6 problems / 2-3 hours	Algorithm efficiency focused, optimization required	Medium-High
Coupang	Online (HackerRank)	3-4 problems / 90 min	Data structures, graphs, strings	Medium
Startups (General)	Take-home / Live Coding	Varies	Practical code, API implementation, code review	Varies

The Samsung SW Aptitude Test pushes implementation skills to the limit. You must solve 2 problems within 3 hours, and the key lies not in the algorithmic difficulty of the problems themselves, but in the ability to accurately implement complex conditions in code. Grid-based simulations, BFS/DFS traversals, and bitmask operations are frequently tested.

Kakao Blind Recruitment draws questions from a wide range of algorithm domains. The earlier problems are relatively straightforward, but the later ones boast significant difficulty. In the 2025 Kakao Blind test, string parsing, tree DP, and network flow application problems were featured, drawing much attention.

Startups tend to favor take-home tests where candidates build actual projects, or live coding sessions with interviewers, rather than algorithm-only problem solving. Typical tasks include REST API implementation, database design, and building simple web applications.

1.2 2026 Question Trends

Several new trends are being observed in 2026 coding tests.

• Increase in AI-Related Problems: Problems involving data preprocessing for ML pipelines, implementing simple recommendation algorithms, and post-processing AI model inference results are emerging. While these don't require training models directly, they evaluate a developer's ability to understand and implement AI-related logic in the AI era.
• Code Review-Style Problems: Problems requiring candidates to find and fix bugs in existing code, or optimize inefficient code, are on the rise. This reflects companies' intent to assess skills actually needed in real-world work.
• Rise of System Design Problems: Especially for experienced hire positions, coding problems related to designing systems that handle large-scale traffic, caching strategies, and database sharding are being featured.
• Composite Problems: Rather than problems solvable with a single algorithm, the proportion of composite problems requiring combinations like graph traversal + DP, or binary search + greedy, is increasing.

2. Mastering Essential Data Structures

2.1 Linear Data Structures

Linear data structures are the absolute foundation of coding tests. You must accurately understand the characteristics and time complexities of each data structure.

Data Structure	Access	Search	Insertion	Deletion	Primary Uses
Array	O(1)	O(n)	O(n)	O(n)	Index-based access, sorting
Linked List	O(n)	O(n)	O(1)	O(1)	Frequent insertions/deletions
Stack	O(n)	O(n)	O(1)	O(1)	Parenthesis matching, DFS, postfix expressions
Queue	O(n)	O(n)	O(1)	O(1)	BFS, task scheduling
Deque	O(n)	O(n)	O(1)	O(1)	Sliding window, bidirectional processing

A Stack is a LIFO (Last In First Out) structure, and is critically used in problems like parenthesis validation, DFS implementation, and finding the largest rectangle in a histogram. A Queue is a FIFO (First In First Out) structure, serving as the fundamental tool for BFS traversal, and used in problems like printer queues and cache management. A Deque allows insertions and deletions at both ends and is extremely useful for sliding window min/max problems.

Let's look at basic patterns for implementing stacks and queues in Python.

# Stack implementation (using list)
stack = []
stack.append(1)    # push
stack.append(2)
stack.append(3)
top = stack.pop()  # pop: 3

# Queue implementation (using collections.deque - faster than list)
from collections import deque
queue = deque()
queue.append(1)       # enqueue
queue.append(2)
queue.append(3)
front = queue.popleft()  # dequeue: 1

# Deque usage - Sliding window maximum
def max_sliding_window(nums, k):
    dq = deque()  # stores indices
    result = []
    for i in range(len(nums)):
        # Remove indices outside the window range
        while dq and dq[0] < i - k + 1:
            dq.popleft()
        # Remove indices of values smaller than current
        while dq and nums[dq[-1]] < nums[i]:
            dq.pop()
        dq.append(i)
        if i >= k - 1:
            result.append(nums[dq[0]])
    return result

2.2 Non-Linear Data Structures

Non-linear data structures are key topics in intermediate and advanced coding tests.

A Tree is a data structure that represents hierarchical data, with Binary Trees, Binary Search Trees (BST), and Heaps being the most important. BSTs have an average time complexity of O(log n) for search, insertion, and deletion. Heaps are used to implement priority queues and are essential for problems like Dijkstra's shortest path and task scheduling.

A Graph is one of the most frequently tested data structures in coding tests. You must clearly understand the differences between Adjacency Matrix and Adjacency List representations. For sparse graphs with many vertices and few edges, adjacency lists are more efficient, while for dense graphs with few vertices and many edges, adjacency matrices are more efficient.

HashMaps/Dictionaries can store and retrieve key-value pairs in O(1), making them useful in countless problems such as frequency counting, duplicate checking, and Two Sum. You should be proficient with Python's dict, collections.Counter, and collections.defaultdict.

# HashMap usage - Two Sum problem
def two_sum(nums, target):
    seen = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in seen:
            return [seen[complement], i]
        seen[num] = i
    return []

# Heap usage - Finding the Kth largest element
import heapq

def find_kth_largest(nums, k):
    # Maintain a min-heap of size k
    min_heap = nums[:k]
    heapq.heapify(min_heap)
    for num in nums[k:]:
        if num > min_heap[0]:
            heapq.heapreplace(min_heap, num)
    return min_heap[0]

# Graph representation - Adjacency list
from collections import defaultdict

graph = defaultdict(list)
edges = [(0, 1), (0, 2), (1, 3), (2, 3), (3, 4)]
for u, v in edges:
    graph[u].append(v)
    graph[v].append(u)  # undirected graph

2.3 Data Structure Usage Frequency (Coding Test Appearance Rate)

Based on an analysis of past coding test problems from major companies in 2025, the appearance frequency by data structure is as follows.

Data Structure	Appearance Frequency	Representative Companies	Study Priority
Array/List	Very High (95%)	All companies	Essential
HashMap/Dictionary	Very High (85%)	Kakao, Naver	Essential
Stack/Queue	High (75%)	Samsung, Kakao	Essential
Graph	High (70%)	Samsung, Naver	Essential
Tree	Medium (55%)	Kakao, LINE	High
Heap/Priority Queue	Medium (50%)	Naver, Coupang	High
Trie	Low (15%)	Kakao	Optional

3. Top 10 Essential Algorithm Patterns

By mastering algorithm patterns that repeatedly appear in coding tests, you can quickly map even unfamiliar problems to known patterns and solve them efficiently.

3.1 Sorting and Searching

Sorting is the foundation of all algorithms. In coding tests, problems that leverage sorting are more common than sorting itself. Python's sorted() and .sort() use TimSort (O(n log n)), so you rarely need to implement sorting algorithms directly, but understanding sorting principles remains important.

Binary Search is an algorithm that finds elements in O(log n) within a sorted array. Beyond simple searches, it is widely used for "finding the minimum/maximum value that satisfies a condition" (parametric search).

# Binary Search - Parametric Search pattern
# Example: Cutting trees - finding the maximum cutter height
def parametric_search(trees, target):
    lo, hi = 0, max(trees)
    result = 0
    while lo <= hi:
        mid = (lo + hi) // 2
        # Total wood obtained when cutting at height mid
        total = sum(max(0, t - mid) for t in trees)
        if total >= target:
            result = mid
            lo = mid + 1
        else:
            hi = mid - 1
    return result

3.2 Brute Force and Backtracking

Brute Force is a method of exploring all possible cases and is the most reliable approach when the input size is small. Backtracking is a technique that improves efficiency by early pruning of unpromising cases during exhaustive search. Typical examples include permutations, combinations, subset generation, and the N-Queen problem.

# Backtracking - Generating combinations
def combinations(n, k):
    result = []
    def backtrack(start, path):
        if len(path) == k:
            result.append(path[:])
            return
        for i in range(start, n + 1):
            path.append(i)
            backtrack(i + 1, path)
            path.pop()  # backtrack
    backtrack(1, [])
    return result

3.3 BFS/DFS (Graph Traversal)

Graph traversal is the most frequently appearing algorithm type in coding tests. BFS (Breadth-First Search) is used for finding shortest distances, while DFS (Depth-First Search) is used for path finding, finding connected components, cycle detection, and more.

# BFS - Shortest path in a maze
from collections import deque

def bfs_shortest_path(grid, start, end):
    rows, cols = len(grid), len(grid[0])
    queue = deque([(start[0], start[1], 0)])  # (row, col, distance)
    visited = set()
    visited.add((start[0], start[1]))
    dx = [-1, 1, 0, 0]
    dy = [0, 0, -1, 1]

    while queue:
        x, y, dist = queue.popleft()
        if (x, y) == (end[0], end[1]):
            return dist
        for i in range(4):
            nx, ny = x + dx[i], y + dy[i]
            if 0 <= nx < rows and 0 <= ny < cols:
                if grid[nx][ny] == 0 and (nx, ny) not in visited:
                    visited.add((nx, ny))
                    queue.append((nx, ny, dist + 1))
    return -1  # unreachable

# DFS - Counting connected components
def count_components(n, edges):
    graph = defaultdict(list)
    for u, v in edges:
        graph[u].append(v)
        graph[v].append(u)
    visited = set()
    count = 0

    def dfs(node):
        visited.add(node)
        for neighbor in graph[node]:
            if neighbor not in visited:
                dfs(neighbor)

    for i in range(n):
        if i not in visited:
            dfs(i)
            count += 1
    return count

3.4 Dynamic Programming (DP)

Dynamic Programming is widely regarded as the most challenging type in coding tests, but once you learn the patterns, the approach becomes much more manageable. The key is identifying Optimal Substructure and Overlapping Subproblems. There are two approaches: top-down (memoization) and bottom-up (tabulation).

# DP - Longest Increasing Subsequence (LIS)
# Top-down (Memoization)
def lis_top_down(nums):
    n = len(nums)
    memo = {}
    def dp(i):
        if i in memo:
            return memo[i]
        result = 1
        for j in range(i):
            if nums[j] < nums[i]:
                result = max(result, dp(j) + 1)
        memo[i] = result
        return result
    return max(dp(i) for i in range(n))

# Bottom-up (Tabulation)
def lis_bottom_up(nums):
    n = len(nums)
    dp = [1] * n
    for i in range(1, n):
        for j in range(i):
            if nums[j] < nums[i]:
                dp[i] = max(dp[i], dp[j] + 1)
    return max(dp)

# O(n log n) optimization - Using binary search
import bisect

def lis_optimized(nums):
    tails = []
    for num in nums:
        pos = bisect.bisect_left(tails, num)
        if pos == len(tails):
            tails.append(num)
        else:
            tails[pos] = num
    return len(tails)

3.5 Greedy Algorithms

A Greedy algorithm achieves a globally optimal solution by making the locally optimal choice at each step. For greedy to guarantee a correct solution, both the greedy choice property and optimal substructure must hold. Typical examples include the activity selection problem, coin change, and interval scheduling.

3.6 Two Pointers / Sliding Window

Two Pointers is a technique that moves two pointers across a sorted array to find pairs satisfying a condition, reducing O(n^2) to O(n). Sliding Window is a technique that moves a fixed or variable-size window across an array to efficiently compute subarray sums, maximums/minimums, and so on.

3.7 Implementation / Simulation

Implementation problems test the ability to accurately translate problem conditions into code, rather than requiring specialized algorithm knowledge. This is the most commonly tested type in the Samsung Aptitude Test, with typical examples including grid rotation, snake games, and robot vacuum cleaner simulations. Practicing careful input parsing, boundary condition handling, and direction-change logic is essential to minimize mistakes.

3.8 String Processing

This is particularly common in Kakao coding tests, with parsing, regular expressions, string comparison, and compression/decompression being key topics. You should be proficient with Python's string slicing, the re module, and built-in str methods. Advanced string algorithms like KMP and Rabin-Karp appear less frequently, but knowing them can be helpful.

3.9 Shortest Path (Dijkstra, Floyd-Warshall)

Dijkstra's algorithm finds the shortest path from a single source to all vertices and is used for graphs with non-negative edge weights. Floyd-Warshall finds the shortest paths between all pairs of vertices with a time complexity of O(V^3).

# Dijkstra - Shortest path
import heapq

def dijkstra(graph, start, n):
    dist = [float('inf')] * n
    dist[start] = 0
    pq = [(0, start)]  # (distance, node)

    while pq:
        d, u = heapq.heappop(pq)
        if d > dist[u]:
            continue
        for v, w in graph[u]:
            if dist[u] + w < dist[v]:
                dist[v] = dist[u] + w
                heapq.heappush(pq, (dist[v], v))
    return dist

3.10 Union-Find / MST

Union-Find (Disjoint Set) is a data structure that efficiently manages disjoint sets, performing Union and Find operations in nearly O(1). It is a core component of Kruskal's algorithm for Minimum Spanning Trees (MST), and appears in problems like network connectivity and minimizing city connection costs.

# Union-Find (Path compression + Rank optimization)
class UnionFind:
    def __init__(self, n):
        self.parent = list(range(n))
        self.rank = [0] * n

    def find(self, x):
        if self.parent[x] != x:
            self.parent[x] = self.find(self.parent[x])  # path compression
        return self.parent[x]

    def union(self, x, y):
        px, py = self.find(x), self.find(y)
        if px == py:
            return False
        if self.rank[px] < self.rank[py]:
            px, py = py, px
        self.parent[py] = px
        if self.rank[px] == self.rank[py]:
            self.rank[px] += 1
        return True

# Kruskal's Algorithm - Minimum Spanning Tree
def kruskal(n, edges):
    edges.sort(key=lambda x: x[2])  # sort by weight
    uf = UnionFind(n)
    mst_cost = 0
    mst_edges = 0
    for u, v, w in edges:
        if uf.union(u, v):
            mst_cost += w
            mst_edges += 1
            if mst_edges == n - 1:
                break
    return mst_cost

4. Language Selection Guide

The language you choose for coding tests can directly impact whether you pass or fail. You should understand the characteristics of each language and make the optimal choice for your situation.

Comparison	Python	Java	C++
Code Length	Short (1x)	Long (2-3x)	Medium (1.5-2x)
Execution Speed	Slow	Fast	Very Fast
Learning Difficulty	Low	Medium	High
Built-in Libraries	Very Rich	Rich	Rich (STL)
Big Number Handling	Auto-supported	BigInteger needed	Manual implementation
Time Limit Compensation	3-5x allowance	2x allowance	Baseline
Debugging Convenience	Excellent	Good	Average

You must also check each company's permitted language list. The Samsung SW Aptitude Test allows only C/C++ and Java -- Python is not permitted. In contrast, Kakao, Naver, LINE, and others accept a variety of languages including Python. If you are applying to Samsung, you must prepare with C++ or Java.

5. Learning Platforms and Roadmaps

5.1 Recommended Platforms

Platform	Language	Problems	Features	Recommended For
Programmers	Korean	2,000+	Kakao past problems, difficulty-based categorization, identical to real test environment	Korean job seekers
Baekjoon (BOJ)	Korean	27,000+	Massive problem library, step-by-step problem sets, solved.ac integration	Systematic learners
LeetCode	English	3,000+	Global standard, FAANG past problems, active discussions	International company applicants
Codeforces	English	8,000+	Regular contests, rating system, high-difficulty problems	Competitive programming enthusiasts
SWEA (Samsung)	Korean	3,000+	Samsung question types, mock aptitude tests available	Samsung applicants

5.2 4-Week / 8-Week / 12-Week Learning Roadmaps

Choose the appropriate roadmap based on your current level and target timeline.

4-Week Intensive Course (3-4 hours/day, 2-3 problems/day)

• Week 1: Basic data structures (arrays, strings, stacks, queues, hashmaps) - 20 problems
• Week 2: Core algorithms (sorting, binary search, brute force, BFS/DFS) - 20 problems
• Week 3: Intermediate algorithms (DP, greedy, two pointers) - 15 problems
• Week 4: Mock tests and past problem practice - 15 problems

8-Week Standard Course (2-3 hours/day, 1-2 problems/day)

• Weeks 1-2: Programming language syntax + basic data structures - 25 problems
• Weeks 3-4: Basic algorithms (sorting, searching, brute force) - 25 problems
• Weeks 5-6: Graphs (BFS/DFS), DP, greedy - 30 problems
• Week 7: Advanced algorithms (shortest path, union-find, strings) - 15 problems
• Week 8: Company-specific past problems + mock tests - 15 problems

12-Week Foundations Course (1-2 hours/day, 1 problem/day)

• Weeks 1-3: Programming language basics + introductory problems - 20 problems
• Weeks 4-6: Basic data structures + basic algorithms - 30 problems
• Weeks 7-9: Intermediate algorithms (DP, graphs, greedy) - 30 problems
• Weeks 10-11: Advanced algorithms + composite problems - 20 problems
• Week 12: Exam prep + weakness reinforcement - 10 problems

5.3 Recommended Books and Courses

• Book: "This Is the Coding Test with Python" by Dong-Bin Na -- The most systematic introductory coding test book in Korea, with a balanced mix of theory and past exam problems.
• Book: "Python Algorithm Interview" by Sang-Gil Park -- Covers LeetCode-based problems solved in Python with detailed code optimization techniques.
• Book: "Algorithmic Problem Solving Strategies" by Jong-Man Gu -- A C++-based advanced algorithm book covering problems up to competitive programming level.
• Online Course: Following Baekjoon's "Step-by-Step Problem Solving" while utilizing the solved.ac difficulty system lets you systematically solve problems suited to your level.
• YouTube: The "Glasses-Wearing Developer Dong-Bin Na" channel visually explains algorithm concepts for easy understanding, while the "Coding Without Code" channel shows detailed real problem-solving processes.

6. Practical Tips & Exam Day Strategies

No matter how skilled you are, your exam day strategy and mental state can determine the outcome. Make sure to internalize the following practical tips.

Time Management Strategy

• Quickly scan all problems first to assess difficulty and estimated time (5 minutes).
• Start with easier problems to secure guaranteed points. For Kakao's 7-problem format, aim to complete the first 3-4 problems within 2 hours.
• If you're stuck on a problem for more than 30 minutes, skip it and move on. Ideas may come to you while solving other problems.
• Reserve the last 20 minutes to review submitted code for edge cases.

Debugging Tips

• Actively use print() debugging. Output key variable values to trace the logic flow.
• Create small test cases by hand, simulate them manually, then compare with code results.
• Off-by-one errors (being one off in range) are the most common bugs. Always double-check loop start points, end points, and indices.

Edge Case Checklist

• Empty input (empty array, empty string)
• Input at minimum or maximum values
• All elements are identical
• Already sorted or reverse-sorted input
• Negative numbers, zero, or very large numbers

Managing Nerves

• Don't stay up late solving problems the night before the exam. Sufficient sleep is crucial for next-day concentration.
• Take 3-5 deep breaths before the exam starts to relieve tension.
• When stuck on a difficult problem, don't panic. Remind yourself: "Even if I can't solve this one, I can make up points on other problems."
• Regularly practice mock tests under real exam conditions (time limits, online grading) to get accustomed to the test environment.

Conclusion: Coding Tests Are About Training

Coding tests are not about innate talent but the result of systematic training. By choosing the right data structures, mastering core algorithm patterns, and consistently solving problems, anyone can reach a passing level. What matters is not solving 10 problems in one day and resting for two, but the sustained effort of solving 1-2 problems daily and reviewing the ones you got wrong.

Here is a summary of the key points covered in this guide. First, identify the coding test format of your target company and build a strategy accordingly. Second, solidify the fundamentals of data structures and algorithms, then expand to pattern-based problem solving. Third, choose your language based on the target company's permitted languages and your own proficiency. Fourth, follow a systematic roadmap using learning platforms, and be sure to supplement it with mock tests under real exam conditions.

Preparing for coding tests is certainly not an easy process, but the algorithmic thinking and problem-solving skills you gain throughout this journey become invaluable assets that you can leverage in your career for a lifetime. As the saying goes, "The first step is half the battle" -- it all begins with solving that first problem today. Consistency becomes skill, and skill becomes results. Start that journey now.