Top 42 Microsoft Coding Interview Questions and Answers

A job at Microsoft is a dream for many tech enthusiasts. Unsurprisingly, less than 2 per cent of software engineers make it through the final round of interviews at Microsoft. The company is at the forefront of innovation, driving transformative changes across software, cloud computing, AI, and beyond. However, getting a job at Microsoft requires passing the technical interview process, where coding proficiency and analytical thinking are tested. Microsoft coding interview questions often involve data structures, algorithms, and problem-solving skills. This blog will help you practice the Microsoft coding interview questions and answers to help you land your dream job.

Microsoft Coding Interview Questions and Answers for Freshers

For freshers preparing to join Microsoft, coding interviews often focus on fundamental concepts and problem-solving skills. Interviewers look for a strong grasp of basic data structures like arrays, linked lists, and trees, along with an understanding of foundational algorithms such as sorting and searching. This section covers the essential Microsoft coding interview questions and answers for freshers. By practicing these questions, freshers can better understand what Microsoft values in its software engineering candidates.

Q1. Write a code to find the sum of all elements in an array.

Sample Answer: To find the sum of all elements in an array, we can iterate through the array and keep a running total of the elements. Each time we encounter a new element, we add it to the total sum. Here’s the code to find the sum:

def sum_of_array(arr):
    total = 0  
    for num in arr:
        total += num  
    return total

array = [1, 2, 3, 4, 5]
print(sum_of_array(array))  # Output: 15

Pro Tip: To implement code in Python during your Microsoft coding interviews, you should be well-versed with topics like Python arrays. This can help you tackle the Microsoft coding round interview questions more effectively.

Q2. Write a program to reverse a string in Python.

Sample Answer: Slicing with [::-1] allows us to reverse the string by stepping through it backward. Here’s the code that helps reverse a string in Python:

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

Q3. Write a code to check if a given number is prime.

Sample Answer: To check if a given number is prime, we need to verify that it has no divisors other than 1 and itself. This code checks divisibility up to the square root of n to determine if it’s prime, improving efficiency.

def is_prime(n):
    if n <= 1:
        return False
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True

Q4. Implement a way to merge two sorted arrays into one sorted array.

Sample Answer: To merge two sorted arrays into a single sorted array, we can use a two-pointer approach. This method iterates through both arrays, comparing their elements one by one, and appending the smaller element to the new merged array. The result is a single, sorted array containing all elements from both original arrays.

Here’s the code to implement this:

def merge_sorted_arrays(arr1, arr2):
    merged_array = []
    i, j = 0, 0

    while i < len(arr1) and j < len(arr2):
        if arr1[i] < arr2[j]:
            merged_array.append(arr1[i])
            i += 1
        else:
            merged_array.append(arr2[j])
            j += 1

    merged_array.extend(arr1[i:])
    merged_array.extend(arr2[j:])

    return merged_array

Q5. Write a code to calculate the factorial of a number.

Sample Answer: The factorial of a number is denoted as n! It is the product of all positive integers from 1 to N. We can calculate it using either an iterative or recursive approach. Here, we will use an iterative approach for simplicity and efficiency.

def factorial(n):
    if n < 0:
        return "Factorial is not defined for negative numbers"
    result = 1
    for i in range(1, n + 1):
        result *= i  
    return result

num = 5
print(factorial(num))  # Output: 120

Q6. Write a code to check if a string is a palindrome.

Sample Answer: To check if a string is a palindrome, verify that it reads the same forwards and backward. This can be done by comparing the string to its reverse. If they are the same, the string is a palindrome.

Here’s a code:

def is_palindrome(s):
    return s == s[::-1]

Q7. Provide a function to find the maximum element in an array.

Sample Answer: To find the maximum element in an array, we can iterate through each element, keeping track of the largest one encountered. By the end of the iteration, we will have the maximum value in the array.

Here’s a code:

def find_max(arr):
    if not arr:
        return "Array is empty"
    
    max_element = arr[0]  
    for num in arr:
        if num > max_element:
            max_element = num  
    return max_element

array = [3, 5, 7, 2, 8]
print(find_max(array))  # Output: 8

Q8. Write a Python code to remove duplicates from a list.

Sample Answer: A list can be automatically filtered using a set, which eliminates duplicate values. Converting the list to a set removes duplicates, and converting it back to a list returns the unique elements. Here’s how this can be done in Python:

def remove_duplicates(lst):
    return list(set(lst))

Q9. Implement a method to find the nth Fibonacci number.

Sample Answer: The Fibonacci sequence is a series of numbers where each number is the sum of the two preceding ones, starting from 0 and 1. To find the nth Fibonacci number, we can use either an iterative or a recursive approach. The following Python method uses recursion to calculate each Fibonacci number by summing the two preceding numbers.

def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)

Pro Tip: The Fibonacci series is commonly asked in entry-level job interviews at Microsoft. Check out our blog on the Fibonacci series in Python to learn about its implementation.

Q10. Provide a way to count the number of vowels in a string.

Sample Answer: To count the number of vowels in a string, we can iterate through each character and check if it is a vowel (a, e, i, o, u) either in uppercase or lowercase. If it is, we increase the count. This solution iterates over the string and counts characters that are vowels.

def count_vowels(s):

    vowels = "aeiouAEIOU"
    count = 0

    for char in s:
        if char in vowels:
            count += 1

    return count

Q11. Write a code block to find the GCD of two numbers.

Sample Answer: The greatest common divisor (GCD) of two numbers is the largest positive integer that divides both numbers without leaving a remainder. This code block uses the Euclidean algorithm, which iteratively sets a to b and b to a % b until b is zero.

def gcd(a, b):
    while b:
        a, b = b, a % b
    return a

Pro Tip: You should be aware of all the Python libraries to answer such coding questions for Microsoft more effectively. Python libraries are quite effective in simplifying complex code implementations.

Q12. Provide a way to check if a number is even or odd.

Sample Answer: To check if a number is even or odd, you can use the modulus operator (%). A number is considered even if it is divisible by 2 (i.e., the remainder when divided by 2 is zero). Conversely, a number is odd if it is not divisible by 2 (i.e., the remainder is one).

Here’s the code to implement this:

def is_even_or_odd(n):

    if n % 2 == 0:
        return "Even"
    else:
        return "Odd"

Q13. Present a way to convert a decimal number to binary.

Sample Answer: The built-in bin() function returns the binary representation of a number prefixed with ‘0b’; slicing [2:] removes it. 

Here’s how the code is implemented:

def decimal_to_binary(n):
    return bin(n)[2:]

Q14. Write a program to find the length of the longest word in a string.

Sample Answer: To find the length of the longest word in a string, we can split the string into words and then determine the length of each word, keeping track of the maximum length encountered. The code splits the string into words and then uses a generator to find the length of the longest word.

Here’s a code:

def longest_word_length(s):
    words = s.split()

        max_length = 0
        for word in words:
        max_length = max(max_length, len(word))
    
    return max_length

Microsoft Coding Interview Questions and Answers for Mid-Level Professionals

While appearing for interviews at Microsoft as a mid-level professional, you would be discussing questions that test your core programming skills and delve deeper into algorithms, and efficient coding practices. Here are Microsoft coding interview questions and answers to help mid-level professionals revisit key programming concepts and ace their interviews.

Q15. Write a code to calculate the power of a number (x^y).

Sample Answer: Using the ** operator in Python calculates x raised to the power of y. 

Here’s a code that calculates the power of a number:

def power(x, y):
    return x ** y

Pro Tip: Python operators are quite useful when dealing with calculations in coding interviews. Learn about the core operators and answer Microsoft coding round job interview questions with logic.

Q16. Write a program to find the second largest element in an array.

Sample Answer: Sorting the array in descending order allows us to return the second element for the second largest. 

Here’s how this can be done in Python:

def second_largest(arr):
    arr = list(set(arr))  # remove duplicates
    arr.sort(reverse=True)
    return arr[1] if len(arr) > 1 else None

Pro Tip: Lists and tuples are used widely for code implementations in Python. Learn about the difference between lists and tuples in Python to answer such Microsoft coding round job interview questions.

Q17. Write a code to find the intersection of two arrays.

Sample Answer: To find the intersection of two arrays, we can utilize Python’s set data structure, which allows for efficient lookups and eliminates duplicates. By converting both arrays to sets and then using the intersection operation, we can obtain the common elements.

def intersect_arrays(arr1, arr2):

    set1 = set(arr1)
    set2 = set(arr2)
    
    intersection = set1.intersection(set2)
    
    return list(intersection)  # Convert the result back to a list

array1 = [1, 2, 3, 4, 5]
array2 = [4, 5, 6, 7, 8]
print(intersect_arrays(array1, array2))  # Output: [4, 5] (order may vary)

Q18. Write a code to check if two strings are anagrams.

Sample Answer: Sorting both strings and comparing allows us to determine if they contain the same characters in any order. The code below checks if the given two strings are anagrams or not.

def are_anagrams(s1, s2):
    return sorted(s1) == sorted(s2)

Q19. Write a function to move all zeros in an array to the end.

Sample Answer: To move all zeros in an array to the end while maintaining the order of non-zero elements, we can use a two-pointer approach. This involves iterating through the array and keeping track of the position to insert non-zero elements.

Here’s a code:

def move_zeros_to_end(arr):

    non_zero_index = 0

    for i in range(len(arr)):
        if arr[i] != 0:

            arr[non_zero_index] = arr[i]
            non_zero_index += 1

    for i in range(non_zero_index, len(arr)):
        arr[i] = 0

    return arr

Q20. Write a function to generate all unique permutations of a string.

Sample Answer: Using itertools.permutations, the function returns all unique arrangements of characters in a string.

Here’s a function to generate all unique permutations of a string:

from itertools import permutations

def unique_permutations(s):
    """
    Generates all unique permutations of a given string.

    Args:
    s (str): The input string.

    Returns:
    set: A set containing all unique permutations of the string.
    """
    return {''.join(p) for p in permutations(s)}

Pro Tip: Coding interview questions asked in Microsoft often test your knowledge of working with strings. Learn more about Python string functions to be prepared for your interviews.

Q21. Implement code to reverse words in a given sentence.

Sample Answer: To reverse the words in a given sentence while maintaining their original order, we can follow these steps:

• Split the sentence into words.
• Reverse the list of words.
• Join the reversed list back into a single string.

The following code splits the sentence, reverses the word list, and joins them to form the reversed sentence.

def reverse_words(sentence):

    words = sentence.split()

    reversed_words = words[::-1]
 
    reversed_sentence = ' '.join(reversed_words)
    
    return reversed_sentence

Q22. Write a code to check if a linked list contains a cycle.

Sample Answer: To check if a linked list contains a cycle, we can use Floyd’s Cycle-Finding Algorithm, known as the ‘tortoise and hare’ method. This code uses Floyd’s cycle-finding algorithm, wherein two pointers move at different speeds; if they meet, there’s a cycle detected during execution.

Here’s a code:

def has_cycle(head):
    slow = fast = head
    while fast and fast.next:
        slow = slow.next
        fast = fast.next.next
        if slow == fast:
            return True
    return False

Q23. Write a code to find the longest common prefix among strings in an array.

Sample Answer: To find the longest common prefix among strings in an array, we can follow these steps:

• Check if the array is empty. If it is, return an empty string.
• Take the first string as a reference for comparison.
• Compare the reference string with each subsequent string character by character.
• Keep track of the longest common prefix found during the comparisons.

The following code compares characters of each string at the same position until the characters differ.

def longest_common_prefix(strs):

    if not strs:
        return ""

    prefix = strs[0]

    for s in strs[1:]:

        while not s.startswith(prefix):
            prefix = prefix[:-1]

            if not prefix:
                return ""

    return prefix

Q24. Write a program to find the first non-repeating character in a string.

Sample Answer: This program uses a dictionary to count characters, the first one with a count of 1 is returned. A dictionary can help to count the occurrences of each character in the string.

We can iterate through the string again to find the first character that has a count of 1 in the dictionary.

def first_non_repeating_char(s):
    count = {}
    for char in s:
        count[char] = count.get(char, 0) + 1
    for char in s:
        if count[char] == 1:
            return char
    return None

Q25. Implement a code to remove all whitespace from a string.

Sample Answer: Using Python’s replace() method can help remove all spaces from the string. Here’s the code that can help remove all whitespace from a string in Python:

def remove_whitespace(s):
    return s.replace(" ", "")

Q26. Write a function to find the missing number in a sequence from 1 to n.

Sample Answer: To find the missing number in a sequence from 1 to N, you can use the formula for the sum of the first N natural numbers. This function uses the formula sum of n*(n+1)//2, the difference with the actual sum yields the missing number.

def find_missing_number(arr, n):

    expected_sum = n * (n + 1) // 2
    
    actual_sum = sum(arr)
    
    missing_number = expected_sum - actual_sum
    
    return missing_number

numbers = [1, 2, 4, 5, 6]  # Missing number is 3
n = 6
print(find_missing_number(numbers, n))  # Output: 3

Q27. Write a program to calculate the depth of a binary tree.

Sample Answer: To calculate the depth (or height) of a binary tree, you can use a recursive approach. A recursive function traverses each node, calculating the maximum depth from root to leaf. 

Here’s how it calculates the depth of a binary tree:

class TreeNode:

    def __init__(self, value=0, left=None, right=None):

        self.value = value
        self.left = left
        self.right = right

def calculate_depth(root):

    if root is None:
        return 0

    left_depth = calculate_depth(root.left)
    right_depth = calculate_depth(root.right)

Q28. Implement code to perform binary search on a sorted array.

Sample Answer: Binary search repeatedly divides the search space in half until it finds the target or confirms it’s not there. 

Here’s the code implementation for a binary search in Python:

def binary_search(arr, target):
    left, right = 0, len(arr) - 1
    while left <= right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    return -1

Microsoft Coding Interview Questions and Answers for Experienced Professionals

In an interview for an experienced professional role at Microsoft, candidates are expected to show a deeper understanding of complex data structures, algorithms, design patterns, and coding efficiency. Questions often focus on code optimization, and knowledge of advanced programming concepts, such as dynamic programming, and tree and graph traversal. Here are a few Microsoft coding round interview questions prepared to reflect the expectations for an experienced-level interview.

Q29. Write code to find the number of occurrences of a target element in a sorted array.

Sample Answer: This code uses the count() method of a list to count occurrences of targets in the array.

def count_occurrences(arr, target):
    return arr.count(target)

Q30. Write a code to check if a string is a valid palindrome, ignoring non-alphanumeric characters and cases.

Sample Answer: To check if a string is a valid palindrome while ignoring non-alphanumeric characters and case sensitivity, we can follow these steps:

• Filter the string to keep only alphanumeric characters.
• Convert the filtered string to a consistent case (either lower or upper).
• Check if the string reads the same forwards and backward.

The following code uses regex to clean the string and then compares it with its reverse.

import re

def is_valid_palindrome(s):
    s = re.sub(r'[^a-zA-Z0-9]', '', s).lower()
    return s == s[::-1]

Q31. Implement a function to find the longest increasing subsequence in an array.

Sample Answer: Dynamic programming stores the longest subsequence length at each index, updating as it finds longer sequences. 

Here’s a function implementing the same in Python:

def longest_increasing_subsequence(arr):

    dp = [1] * len(arr)

    for i in range(1, len(arr)):
        for j in range(i):
            if arr[i] > arr[j]:
                dp[i] = max(dp[i], dp[j] + 1)

    return max(dp)

Q32. Write a function to detect a cycle in a directed graph using Depth First Search (DFS).

Sample Answer: To detect a cycle in a directed graph using Depth First Search (DFS), we can utilize a coloring approach. The basic idea is to maintain a state for each vertex:

• 0: Not visited
• 1: Visiting (currently in the recursion stack)
• 2: Fully visited (processed)

When we encounter a vertex that is in the ‘Visiting’ state while traversing, it indicates a cycle.

Here’s how to implement this in Python:

def detect_cycle(graph):

    visited = [0] * len(graph)

    def dfs(node):

        if visited[node] == 1:  
            return True
        if visited[node] == 2: 
            return False
        
        visited[node] = 1
        
        for neighbor in graph[node]:
            if dfs(neighbor):  
                return True
        
        visited[node] = 2
        return False

    for i in range(len(graph)):
        if visited[i] == 0:  
            if dfs(i):

                return True 

    return False 

Pro Tip: The key features of Python programming make working with graphs much simpler. Having the right skill set to use these features can help you crack your Microsoft coding interviews.

Q33. Write a function to flatten a nested dictionary.

Sample Answer: To flatten a nested dictionary, we can use recursion to traverse through each key-value pair. If a value is another dictionary, we will recursively call the function to flatten it, appending the nested keys to the parent keys to form a new flattened key.

Here’s how you can implement this in Python:

def flatten_dict(nested_dict, parent_key='', sep='_'):
    items = {}
    
    for key, value in nested_dict.items():
        new_key = f"{parent_key}{sep}{key}" if parent_key else key
        
        if isinstance(value, dict):
            items.update(flatten_dict(value, new_key, sep=sep))
        else:
            items[new_key] = value
    
    return items

Q34. Write a program to serialize and deserialize a binary tree.

Sample Answer: To serialize and deserialize a binary tree, we can use a preorder traversal approach. During serialization, we traverse the tree and record the values of the nodes, including markers for None values to indicate empty nodes. During deserialization, we reconstruct the tree using the recorded values.

Here’s how to implement serialization and deserialization for a binary tree in Python:

class TreeNode:
    def __init__(self, value=0, left=None, right=None):
        self.value = value
        self.left = left
        self.right = right

class Codec:
    def serialize(self, root):
        """Encodes a tree to a single string."""
        def recurse(node):
            if not node:
                return 'None,'
            return str(node.value) + ',' + recurse(node.left) + recurse(node.right)
        
        return recurse(root)

    def deserialize(self, data):
        """Decodes your encoded data to tree."""
        def recurse(data_list):
            if data_list[0] == 'None':
                data_list.pop(0) 
                return None
            node = TreeNode(int(data_list[0]))
            data_list.pop(0) 
            node.left = recurse(data_list)
            node.right = recurse(data_list)
            return node
        
        data_list = data.split(',')
        return recurse(data_list[:-1])  

Q35. Implement a solution to calculate the shortest path in a weighted graph using Dijkstra’s algorithm.

Sample Answer: We can implement Dijkstra’s algorithm to calculate the shortest path in a weighted graph. This algorithm works by maintaining a priority queue (often implemented with a min-heap) to repeatedly extract the vertex with the smallest tentative distance, updating the distances of its neighboring vertices.

Here’s a Python implementation of Dijkstra’s algorithm using a priority queue:

import heapq

def dijkstra(graph, start):
    distances = {vertex: float('infinity') for vertex in graph}
    distances[start] = 0
    priority_queue = [(0, start)]
    
    while priority_queue:
        current_distance, current_vertex = heapq.heappop(priority_queue)
        if current_distance > distances[current_vertex]:
            continue
        for neighbor, weight in graph[current_vertex].items():
            distance = current_distance + weight
            if distance < distances[neighbor]:
                distances[neighbor] = distance
                heapq.heappush(priority_queue, (distance, neighbor))
    return distances

Q36. Design a system that implements polymorphism to calculate the area of different shapes (circle and rectangle).

Sample Answer: Polymorphism is an OOPS concept in Python. It is implemented using a base Shape class, and subclasses override the area() method to provide unique calculations.

class Shape:
    def area(self):
        pass

class Circle(Shape):
    def __init__(self, radius):
        self.radius = radius
    
    def area(self):
        return 3.14159 * self.radius * self.radius

class Rectangle(Shape):
    def __init__(self, width, height):
        self.width = width
        self.height = height
    
    def area(self):
        return self.width * self.height
    
shapes = [Circle(5), Rectangle(4, 6)]
areas = [shape.area() for shape in shapes]

Q37. Write code to find all paths from the root to leaf nodes in a binary tree.

Sample Answer: To find all paths from the root to the leaf nodes in a binary tree, use a depth-first search (DFS) approach. The idea is to traverse the tree, keeping track of the current path from the root to the current node. When we reach a leaf node (a node with no children), we can add the current path to our list of paths.

def find_paths(root):
    paths = []
    def dfs(node, current_path):
        if node:
            current_path.append(node.val)
            if not node.left and not node.right:
                paths.append(list(current_path))
            else:
                dfs(node.left, current_path)
                dfs(node.right, current_path)
            current_path.pop()
    dfs(root, [])
    return paths

Pro List: Lists usually are disucssed to test your knowledge of data structures in the coding interview rounds. Prepare the Microsoft coding round job interview questions by learning about Python list functions.

Q38. Implement a method to find the minimum spanning tree of a graph using Kruskal’s algorithm.

Sample Answer: Kruskal’s algorithm sorts edges by weight and uses union-find to add edges without forming cycles. 

Here’s a method to find the minimum spanning tree of a graph using Kruskal’s algorithm:

def kruskal(graph):
    parent = {}
    def find(v):
        if parent[v] != v:
            parent[v] = find(parent[v])
        return parent[v]

    def union(v1, v2):
        root1, root2 = find(v1), find(v2)
        parent[root2] = root1

    edges = sorted(graph['edges'], key=lambda x: x[2])
    mst = []
    for vertex in graph['vertices']:
        parent[vertex] = vertex
    for v1, v2, weight in edges:
        if find(v1) != find(v2):
            union(v1, v2)
            mst.append((v1, v2, weight))
    return mst

Q39. Write a function to detect palindrome partitions in a string.

Sample Answer: To detect palindrome partitions in a string, use a backtracking approach. Using backtracking, this function divides the string into palindromic segments and stores all valid partitions.

Here’s a function to detect palinfrome partitions in a string:

def palindrome_partitions(s):
    result = []
    def backtrack(start, path):
        if start == len(s):
            result.append(list(path))
        for end in range(start + 1, len(s) + 1):
            if s[start:end] == s[start:end][::-1]:
                path.append(s[start:end])
                backtrack(end, path)
                path.pop()
    backtrack(0, [])
    return result

Q40. Write code to implement a Least Recently Used (LRU) Cache.

Sample Answer: To implement a Least Recently Used (LRU) Cache, use a combination of a hash map (dictionary) and a doubly linked list. The hash map will provide fast access to cache items, while the doubly linked list will maintain the order of usage for the cache items, allowing us to efficiently remove the least recently used item when the cache reaches its capacity.

This code shows how to implement an LRU Cache in Python:

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)

Pro Tip: Classes and objects become the backbone of coding problems during interviews for software engineering roles. Therefore, the knowledge of Python classes and objects is essential to crack the coding interviews of Microsoft.

Q41. Implement code to balance a Binary Search Tree (BST).

Sample Answer: A Binary Search Tree (BST) is a data structure that maintains the properties of a binary tree, with the following characteristics:

• Node Structure: Each node contains a value and two pointers, one pointing to the left child and the other to the right child.
• Ordering Property: The left subtree of a node contains only nodes with values less than the node’s value. On the other hand, the right subtree of a node contains only nodes with values greater than the node’s value.
• No Duplicates: Each value in a BST is unique, which allows for efficient searching, insertion, and deletion operations.

Here’s a code to balance a binary search tree:

def balance_bst(root):
    def inorder(node):
        return inorder(node.left) + [node] + inorder(node.right) if node else []
    def build_balanced_tree(nodes):
        if not nodes:
            return None
        mid = len(nodes) // 2
        root = nodes[mid]
        root.left = build_balanced_tree(nodes[:mid])
        root.right = build_balanced_tree(nodes[mid+1:])
        return root
    return build_balanced_tree(inorder(root))

Q42. Write a function to calculate the number of islands in a grid.

Sample Answer: To calculate the number of islands in a grid, we can use a depth-first search (DFS) or breadth-first search (BFS) approach. An island is defined as a group of connected ‘1’s (land) surrounded by ‘0’s (water). The algorithm will traverse the grid and mark all the cells of an island as visited when we find a ‘1’.

Here’s a function to calculate the number of islands in a grid:

def num_islands(grid):
    def dfs(i, j):
        if i < 0 or i >= len(grid) or j < 0 or j >= len(grid[0]) or grid[i][j] != '1':
            return
        grid[i][j] = '0'
        dfs(i+1, j), dfs(i-1, j), dfs(i, j+1), dfs(i, j-1)

    count = 0
    for i in range(len(grid)):
        for j in range(len(grid[0])):
            if grid[i][j] == '1':
                dfs(i, j)
                count += 1
    return count

Tips to Ace Microsoft Coding Interview Questions

Learning how to get a job at Microsoft requires a strategic approach, particularly when showcasing your technical skills and understanding of key programming concepts. Here are some effective tips to prepare for a Microsoft coding interview:

1. Build Relevant Projects: Engaging in projects that reflect your problem-solving skills is essential. Focus on creating applications that demonstrate your understanding of popular Microsoft technologies or languages, such as Python, C#, .NET, or Azure. 
2. Leverage Microsoft Technologies: Familiarize yourself with Microsoft-specific technologies, such as Azure services, Visual Studio, and the Microsoft ecosystem’s various libraries and frameworks. Understanding how these technologies integrate and support development can help you stand out. 
3. Practice Functions and Algorithms: Mastering core algorithms and data structures is important for coding interviews. Regularly practice solving problems related to arrays, strings, trees, and graphs. 
4. Participate in Mock Interviews: Conducting mock interviews with peers or using platforms designed for technical interview preparation can be highly beneficial. This practice allows you to simulate the interview environment and improve your communication skills. 
5. Research About Microsoft’s Culture and Values: Understanding Microsoft’s work culture and values can give you a competitive edge. Research the company’s mission, vision, and core principles, and think about how your skills and experiences align with them. Being able to discuss how you embody these values during your interview can enhance your chances of landing a job.

Conclusion

A coding interview at Microsoft can be the opportunity to demonstrate technical skills and problem-solving abilities. Additionally, Microsoft coding interview questions and answers can check how you can align with the company’s work culture. Your understanding of core programming concepts and relevant technologies will lead to your success in having a job at Microsoft. By practicing these coding problems and familiarizing yourself with Microsoft’s interview format, you can enhance your chances of successfully cracking your interviews. Want to learn about getting a job at Microsoft as a software engineer? Refer to our blog on how to get a job at Microsoft as a software engineer to land a job at the company.

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