Licious Coding Questions 2025-2026 | Placement Interview Problems with Solutions

Licious’s coding round evaluates DSA and problem-solving for product and supply-chain engineering roles. The process typically includes an online assessment (e.g. 90 minutes) with 2–3 coding problems (easy to medium) and technical interviews with DSA and sometimes system design. They test arrays, strings, hash maps, and basic algorithms; they value correct code and clear thinking.

Licious Coding Round at a Glance

Format: OA with 2–3 coding problems; technical rounds with DSA, possibly system design. Difficulty: Easy to medium. Topics: Arrays, strings, hash maps, basic DSA. Focus: Correctness, approach, and communication.

Coding Questions - Complete Problem Set

Practice coding interview questions with solutions.

Arrays & Strings

1. Two Sum

def two_sum(nums, target):
    seen = {}
    for i, num in enumerate(nums):
        if target - num in seen:
            return [seen[target - num], i]
        seen[num] = i
    return []

2. Valid Parentheses

def is_valid(s):
    stack = []
    mapping = {')': '(', '}': '{', ']': '['}
    for char in s:
        if char in mapping:
            top = stack.pop() if stack else '#'
            if mapping[char] != top:
                return False
        else:
            stack.append(char)
    return not stack

3. Reverse String

def reverse_string(s):
    return s[::-1]

4. Palindrome Check

def is_palindrome(s):
    s = ''.join(c.lower() for c in s if c.isalnum())
    return s == s[::-1]

5. Merge Intervals

def merge(intervals):
    intervals.sort(key=lambda x: x[0])
    merged = []
    for interval in intervals:
        if not merged or merged[-1][1] < interval[0]:
            merged.append(interval)
        else:
            merged[-1][1] = max(merged[-1][1], interval[1])
    return merged

6. Maximum Subarray

def max_subarray(nums):
    max_sum = current_sum = nums[0]
    for num in nums[1:]:
        current_sum = max(num, current_sum + num)
        max_sum = max(max_sum, current_sum)
    return max_sum

7. LRU Cache

from collections import OrderedDict

class LRUCache:
    def __init__(self, capacity):
        self.cache = OrderedDict()
        self.capacity = capacity

    def get(self, key):
        if key not in self.cache:
            return -1
        self.cache.move_to_end(key)
        return self.cache[key]

    def put(self, key, value):
        if key in self.cache:
            self.cache.move_to_end(key)
        self.cache[key] = value
        if len(self.cache) > self.capacity:
            self.cache.popitem(last=False)

Company-Specific Focus Areas

Licious often emphasizes arrays (two sum, subarray, merge intervals), strings (validation, manipulation), hash maps and sets, and basic greedy or DP. Supply-chain or product context may show up in problem framing. Focus on clean code and edge cases; system design may appear in later technical rounds.

Related Resources

Interview Experience

Real interview experiences.

Preparation Guide

Complete preparation strategy.

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Practice these problems and focus on arrays, strings, and hash-based solutions for Licious’s coding round.

Last updated: February 2026