Welcome to an advanced tutorial designed for developers, focusing on challenging algorithmic pseudo-code questions. It doesn’t matter if you are a C#, .NET, Python, C, or Java developer. In this guide, we will explore questions that will push your problem-solving abilities and encourage creative thinking.

We’ve categorized them into four sections: Advanced Logic, Complex String Manipulation, Sophisticated Data Structures, and Algorithmic Brain Teasers. Each section presents five questions, followed by detailed pseudo-code answers.

Also Read: 20 Common .NET Coding Interview Questions with Answers

## Brainstorm with 20 Developer Pseudo Code Questions

Let’s dive into 20 thought-provoking Developer Coding Questions that will spark creativity and enhance your problem-solving prowess.

### Advanced Logic

#### Prime Number Checker

**Question:**

Write code to determine if a given number is a prime number.

In this code, we’re figuring out if a number is prime. We loop up to its square root, checking if it’s divisible by any number. This method helps us quickly determine if a number is prime.

```
# Answer
is_prime = true
for i from 2 to square_root(num)
if num is divisible by i
set is_prime to false
break
print "Is prime:", is_prime
```

#### Greatest Common Divisor

**Question:**

Create a test code to find the greatest common divisor (GCD) of two numbers.

Here, we’re finding the common factor of two numbers, also known as the Greatest Common Divisor (GCD). We do this by repeatedly swapping and updating remainders until one becomes zero. This clever method ensures we find the GCD efficiently.

```
# Answer
while num2 not equals 0
temp = num2
num2 = num1 % num2
num1 = temp
print "GCD:", num1
```

#### Unique Paths in a Grid

**Question:**

Write a simple code to calculate the number of unique paths from the top-left corner to the bottom-right corner in a grid, where movement is allowed only right or down.

This code calculates the number of unique paths in a grid. We use dynamic programming, filling a grid with counts of unique paths, considering only right and down movements.

```
# Answer
initialize a 2D array grid with dimensions rows x columns
for i from 0 to rows-1
for j from 0 to columns-1
if i equals 0 or j equals 0
grid[i][j] = 1
else
grid[i][j] = grid[i-1][j] + grid[i][j-1]
print "Unique paths:", grid[rows-1][columns-1]
```

#### Subarray Sum Equals K

**Question:**

Create an algo to determine if there exists a subarray with the sum equal to a given target value K.

Here, we’re creating an algorithm to check if there exists a subarray with the sum equal to a given target value, K. We use a hashmap to keep track of cumulative sums and efficiently find the subarrays.

```
# Answer
sum_map = {}
current_sum = 0
count = 0
for each element in array
current_sum += element
if current_sum equals K
count += 1
if (current_sum - K) exists in sum_map
count += sum_map[current_sum - K]
increment sum_map[current_sum] by 1
print "Subarrays with sum K:", count
```

#### Longest Increasing Subsequence

**Question:**

Write pseudo code to find the length of the longest increasing subsequence in an array of integers.

In this code, we’re figuring out how long the increasing chain of numbers is in a list. We go through the list, keeping track of the length based on the previous numbers. It’s a cool dynamic programming solution.

```
# Answer
lis = [1] * length(array)
for i from 1 to length(array)-1
for j from 0 to i-1
if array[i] > array[j] and lis[i] < lis[j] + 1
lis[i] = lis[j] + 1
print "Longest Increasing Subsequence Length:", maximum(lis)
```

### Complex String Manipulation

#### Regular Expression Matching

**Question:**

Create a simple code to implement regular expression matching with support for ‘*’ and ‘.’.

This code checks if a string matches a pattern with ” and ‘.’. It cleverly uses a recursive function to handle different cases, focusing on the first character and dealing with ” by calling itself recursively.

```
# Answer
function is_match(s, p)
if length(p) equals 0
return length(s) equals 0
first_match = (length(s) not equals 0) and (s[0] equals p[0] or p[0] equals '.')
if length(p) >= 2 and p[1] equals '*'
return is_match(s, p[2:]) or (first_match and is_match(s[1:], p))
else
return first_match and is_match(s[1:], p[1:])
```

#### Palindrome Partitioning

**Question:**

Write an algo to partition a string into palindrome substrings.

In this code, we’re breaking a word into parts that read the same backward and forward. The code does this by checking and storing valid palindrome partitions through a recursive process.

```
# Answer
function partition_palindromes(s)
result = []
backtrack([], s)
return result
function backtrack(path, s)
if length(s) equals 0
result.append(path)
return
for i from 1 to length(s)
if is_palindrome(s[:i])
backtrack(path + [s[:i]], s[i:])
function is_palindrome(s)
return s equals reverse_string(s)
```

#### Minimum Window Substring

**Question:**

Create a simple algorithm to find the minimum window in a string that contains all characters of another string.

In this code, we find the minimum window in a string containing all characters of another string. It’s a sliding window approach, where we maintain a character count and adjust the window boundaries.

```
# Answer
function min_window(s, t)
char_count = initialize a map with character counts in t
required_chars = length(char_count)
left = 0
right = 0
formed_chars = 0
result = ""
while right < length(s)
if s[right] in char_count
char_count[s[right]] -= 1
if char_count[s[right]] equals 0
formed_chars += 1
while formed_chars equals required_chars
if result equals "" or (right - left + 1) < length(result)
result = s[left:right+1]
if s[left] in char_count
char_count[s[left]] += 1
if char_count[s[left]] > 0
formed_chars -= 1
left += 1
right += 1
return result
```

#### Longest Common Subsequence

**Question:**

Write a basic algorithm to find the length of the longest common subsequence of two strings.

Here, we calculate the length of the longest common subsequence between two strings. We use dynamic programming and a 2D array to efficiently find this common sequence.

```
# Answer
function longest_common_subsequence(s1, s2)
m = length(s1)
n = length(s2)
lcs = initialize a 2D array with dimensions m+1 x n+1
for i from 0 to m
for j from 0 to n
if i equals 0 or j equals 0
lcs[i][j] = 0
else if s1[i-1] equals s2[j-1]
lcs[i][j] = lcs[i-1][j-1] + 1
else
lcs[i][j] = maximum(lcs[i-1][j], lcs[i][j-1])
return lcs[m][n]
```

#### KMP String Matching Algorithm

**Question:**

Provide a working code to implement the Knuth-Morris-Pratt (KMP) string-matching algorithm.

This code implements the Knuth-Morris-Pratt (KMP) string-matching algorithm. We build a prefix table and use it to search for occurrences in a string.

```
# Answer
function build_kmp_table(pattern)
table = initialize an array of length pattern
len = 0
i = 1
while i < length(pattern)
if pattern[i] equals pattern[len]
len += 1
table[i] = len
i += 1
else
if len not equals 0
len = table[len-1]
else
table[i] = 0
i += 1
return table
function kmp_search(text, pattern)
m = length(text)
n = length(pattern)
lps = build_kmp_table(pattern)
i = 0
j = 0
while i < m
if pattern[j] equals text[i]
i += 1
j += 1
if j equals n
print "Pattern found at index:", i-j
j = lps[j-1]
else if i < m and pattern[j] not equals text[i]
if j not equals 0
j = lps[j-1]
else
i += 1
```

### Sophisticated Data Structures

#### Trie Implementation

**Question:**

Provide a step-by-step logic to implement a trie data structure.

In this code, we’re setting up a Trie data structure. It includes adding words, searching for complete words, and checking if a word starts with a given prefix. Tries are efficient for storing and retrieving words.

```
# Answer
class TrieNode
end = false
kids = initialize an array of TrieNode
class Trie
root = TrieNode
function add(w)
cur = root
for c in w
cur = cur.kids[c] ? cur.kids[c] : (cur.kids[c] = new TrieNode)
cur.end = true
function find(w)
cur = root
for c in w
if not cur.kids[c] return false
cur = cur.kids[c]
return cur.end
function startsWith(pre)
cur = root
for c in pre
if not cur.kids[c] return false
cur = cur.kids[c]
return true
```

#### Union Find (Disjoint Set) Implementation

**Question:**

Create pseudo code to implement the Union-Find (Disjoint Set) data structure.

Here, we implement the Union-Find (Disjoint Set) data structure. It includes finding the root of a set and uniting two sets. It’s useful for tasks like connectivity checks.

```
# Answer
class UnionFind
parent = initialize an array with each element as its own parent
function find(x)
if parent[x] equals x
return x
parent[x] = find(parent[x])
return parent[x]
function union(x, y)
root_x = find(x)
root_y = find(y)
parent[root_x] = root_y
# Usage Example
uf = new UnionFind
uf.union(1, 2)
uf.union(2, 3)
print "Are 1 and 3 connected?", uf.find(1) equals uf.find(3)
```

#### AVL Tree Rotation

**Question:**

Write a simple logic to perform a right rotation in an AVL tree.

This code performs a right rotation in an AVL tree. AVL trees maintain balance in binary search trees, and rotations help keep them balanced.

```
# Answer
class TreeNode
data
height
left
right
function right_rotate(y)
x = y.left
T = x.right
x.right = y
y.left = T
y.height = maximum(height(y.left), height(y.right)) + 1
x.height = maximum(height(x.left), height(x.right)) + 1
return x
```

#### Priority Queue Implementation

**Question:**

Create pseudo code to implement a priority queue using a binary heap.

In this code, we create a Priority Queue using a binary heap. It allows efficient insertion and removal of elements based on their priority. Priority queues are handy in various algorithms.

```
# Answer
class PriorityQueue
heap = []
function push(value)
append value to heap
index = length(heap) - 1
while index > 0
parent_index = (index - 1) / 2
if heap[index] < heap[parent_index]
swap heap[index] and heap[parent_index]
index = parent_index
else
break
function pop()
if length(heap) equals 0
return "Priority Queue is empty"
if length(heap) equals 1
return pop element from heap
root = heap[0]
heap[0] = remove last element from heap
index = 0
while true
left_child = 2 * index + 1
right_child = 2 * index + 2
min_index = index
if left_child < length(heap) and heap[left_child] < heap[min_index]
min_index = left_child
if right_child < length(heap) and heap[right_child] < heap[min_index]
min_index = right_child
if min_index not equals index
swap heap[index] and heap[min_index]
index = min_index
else
break
return root
```

#### Graph Representation (Adjacency List)

**Question:**

How to represent an undirected graph using an adjacency list.

This code represents an undirected graph using an adjacency list. It demonstrates adding vertices, and edges and provides a basic structure for graph-related algorithms.

```
# Answer
class Graph
vertices = initialize an empty dictionary
function add_vertex(vertex)
if vertex not in vertices
vertices[vertex] = []
function add_edge(vertex1, vertex2)
vertices[vertex1].append(vertex2)
vertices[vertex2].append(vertex1)
```

### Algorithmic Brain Teasers

#### Trapping Rain Water

**Question:**

Create a pseudo code to find the amount of water that can be trapped between given heights.

Here, we find the amount of water that can be trapped between given heights. The code uses a stack to keep track of potential boundaries for trapping water, solving a common algorithmic problem.

```
function trap(height)
stack = []
waterTrapped = 0
for currentIdx from 0 to length(height) - 1
while length(stack) > 0 and height[currentIdx] > height[stack[top(stack)]]
topIdx = stack.pop()
if length(stack) = 0
break
distance = currentIdx - stack[top(stack)] - 1
minHeight = minimum(height[currentIdx], height[stack[top(stack)]]) - height[topIdx]
waterTrapped += distance * minHeight
stack.push(currentIdx)
return waterTrapped
function top(stack)
return length(stack) - 1
```

#### Hamiltonian Cycle

**Question:**

Provide the logic to find a Hamiltonian Cycle in a given undirected graph.

In this code, we explore finding a Hamiltonian Cycle in a given undirected graph. The code uses backtracking to search for a cycle that visits each vertex exactly once.

```
# Answer
function hamiltonian_cycle(graph)
path = []
visited = initialize a set of visited vertices
path.append(starting_vertex)
visited.add(starting_vertex)
if hamiltonian_util(graph, path, visited)
print "Hamiltonian Cycle:", path
else
print "No Hamiltonian Cycle"
function hamiltonian_util(graph, path, visited)
if length(path) equals number_of_vertices_in_graph
return graph[path[-1]] contains path[0]
for each neighbor in graph[path[-1]]
if neighbor not in visited
path.append(neighbor)
visited.add(neighbor)
if hamiltonian_util(graph, path, visited)
return true
path.pop()
visited.remove(neighbor)
return false
```

#### Dijkstra’s Shortest Path

**Question:**

Create pseudo code to find the shortest path in a weighted graph using Dijkstra’s algorithm.

In this code, we’re using Dijkstra’s algorithm to discover the quickest route in a graph with different path lengths. The code smartly explores and updates the shortest distances starting from a specific point.

```
# Answer
function dijkstra(graph, start)
distances = initialize a map with infinite distances for all vertices
distances[start] = 0
priority_queue = create a priority queue with vertices and their distances
priority_queue.push((0, start))
while priority_queue not empty
current_distance, current_vertex = priority_queue.pop()
if current_distance > distances[current_vertex]
continue
for neighbor, weight in graph[current_vertex]
distance = current_distance + weight
if distance < distances[neighbor]
distances[neighbor] = distance
priority_queue.push((distance, neighbor))
return distances
```

#### Traveling

Salesman Problem**Question:**

Provide an algo to solve the Traveling Salesman Problem using dynamic programming.

This algorithm tackles the Traveling Salesman Problem using dynamic programming. It efficiently explores all possible paths to find the shortest tour that visits each city exactly once.

```
# Answer
function tsp(graph, start)
n = number_of_vertices_in_graph
all_sets = 2^n
memo = initialize a memoization table with dimensions n x all_sets
return tsp_util(graph, start, 1, memo)
function tsp_util(graph, current, mask, memo)
if mask equals (1 << n) - 1
return graph[current][start]
if memo[current][mask] not equals undefined
return memo[current][mask]
result = infinity
for next_vertex in range(n)
if (mask >> next_vertex) and 1 equals 0
continue
result = minimum(result, graph[current][next_vertex] + tsp_util(graph, next_vertex, mask | (1 << next_vertex), memo))
memo[current][mask] = result
return result
```

#### Sudoku Solver

**Question:**

Create pseudo code to solve a Sudoku puzzle.

In this code, we’re solving a Sudoku puzzle by filling in the empty spaces. The algorithm works efficiently, making sure that every row, column, and small box has numbers from 1 to 9 without repeating.

```
# Answer
function solve_sudoku(board)
empty_cell = find_empty_cell(board)
if not empty_cell
return true
row, col = empty_cell
for num in range(1, 10)
if is_safe(board, row, col, num)
board[row][col] = num
if solve_sudoku(board)
return true
board[row][col] = 0
return false
function is_safe(board, row, col, num)
return not in_row(board, row, num) and not in_col(board, col, num) and not in_box(board, row - row % 3, col - col % 3, num)
function in_row(board, row, num)
return num in board[row]
function in_col(board, col, num)
return num in [board[i][col] for i in range(9)]
function in_box(board, start_row, start_col, num)
return num in [board[start_row + i][start_col + j] for i in range(3) for j in range(3)]
```

### Conclusion – 20 Pseudo Code Questions With Answers

Congratulations on completing the challenging developer algorithmic problems. These advanced problems were designed to stimulate your problem-solving skills and enhance your ability to think algorithmically.

Practice these exercises to strengthen your proficiency. It will enable you to approach complex coding challenges with confidence.

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**Enjoy coding,TechBeamers.**