Top 30 Google Software Engineer Interview Questions and Answers

Did you know that Google receives millions of job applications every year? With competition so fierce, it’s essential to be well-prepared to shine in your interview. Google’s software engineering interviews involve several rounds designed to evaluate both your technical skills and your ability to work well with others. In this blog, we’ll explore 30 common Google software engineer interview questions and answers, categorized for freshers, mid-level, and experienced professionals. By understanding these questions and preparing thoughtful answers, you can land your dream job at Google.

Google Software Engineer Interview Questions and Answers for Freshers

For freshers entering the tech industry, the Google software engineer interview can be both exciting and intimidating. At this level, interviewers usually assess candidates on their foundational knowledge of computer science principles and basic coding skills. This section focuses on the common Google software engineer technical interview questions and answers that freshers may face.

Q1. Decode a given encoded string and return the result.

Sample Answer: When tasked with decoding an encoded string, it is essential to first understand the encoding scheme used. For instance, if the string is encoded using a simple character shift (like in a Caesar cipher), we would need to reverse that shift. A sample solution in Python could involve iterating through each character in the string, applying the inverse operation of the encoding process, and then returning the decoded result. 

Here’s how you might implement it:

def decode_string(encoded_str):
    decoded_str = ''
    For char in encoded_str:
        decoded_str += chr(ord(char) - 1)  # Example: shifting characters back by 1
    return decoded_str

Q2. Check if a typed string could result from long-pressing some characters of a given name.

Sample Answer: To determine if a typed string could be the result of long-pressing characters from a given name, we can use a two-pointer technique. By iterating through both strings simultaneously, we can check if every character in the typed string appears in sequence within the name, allowing for repeated characters. 

Here’s a sample implementation:

def is_long_pressed_name(name, typed):
    i = j = 0
    while j < len(typed):
        if i < len(name) and name[i] == typed[j]:
            i += 1
        elif j == 0 or typed[j] != typed[j - 1]:
            return False
        j += 1
    return i == len(name)

This function returns True if the typed string can be formed by long-pressing characters from the name; otherwise, it returns False.

Also Read: Google Interview Questions

Q3. Identify the smallest substring of a string s that contains all characters of another string t in o(n) time complexity.

Sample Answer: To find the smallest substring containing all characters of another string efficiently, we can use a sliding window approach along with a hashmap to track character counts. This ensures we maintain an O(n) time complexity. 

Here’s how you can implement it:

from collections import Counter

def min_window_substring(s, t):
    t_count = Counter(t)
    current_count = {}
    l, r = 0, 0
    min_length = float('inf')
    min_substr = ""

    while r < len(s):
        current_count[s[r]] = current_count.get(s[r], 0) + 1
        
        while all(current_count.get(char, 0) >= count for char, count in t_count.items()):
            if r - l + 1 < min_length:
                min_length = r - l + 1
                min_substr = s[l:r + 1]
            current_count[s[l]] -= 1
            if current_count[s[l]] == 0:
                del current_count[s[l]]
            l += 1
        r += 1
    
    return min_substr

Q4. Add two numbers represented by non-empty linked lists in reverse order and return the sum as a linked list.

Sample Answer: To do this, we can iterate through both lists simultaneously, summing corresponding digits and managing carry-over as needed. Here’s an example implementation:

class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next

def add_two_numbers(l1, l2):
    dummy_head = ListNode(0)
    p, q, current = l1, l2, dummy_head
    carry = 0
    
    while p or q or carry:
        x = p.val if p else 0
        y = q.val if q else 0
        total = carry + x + y
        carry = total // 10
        current.next = ListNode(total % 10)
        
        current = current.next
        if p: p = p.next
        if q: q = q.next
        
    return dummy_head.next

Q5. Calculate how many confusing numbers exist between 1 and n.

Sample Answer: A confusing number transforms into a valid but different number when rotated 180 degrees. To count how many confusing numbers exist between 1 and N, we can iterate through each number, check its validity upon rotation, and maintain a count. Here’s how this can be implemented:

def is_confusing_number(num):
    rotate_map = {0: 0, 1: 1, 6: 9, 8: 8, 9: 6}
    rotated_num = ''
    
    while num > 0:
        digit = num % 10
        if digit not in rotate_map:
            return False
        rotated_num += str(rotate_map[digit])
        num //= 10
    
    return int(rotated_num) != int(str(num))

def confusing_numbers_count(N):
    count = sum(1 for i in range(1, N + 1) if is_confusing_number(i))
    return count

Q6. Determine the maximum path sum in a binary tree.

Sample Answer: To find the maximum path sum in a binary tree where paths can start and end at any node, we can use recursion to calculate sums for each node while keeping track of the maximum found. Here’s an example implementation:

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

def max_path_sum(root):
    max_sum = float('-inf')

    def helper(node):
        nonlocal max_sum
        if not node:
            return 0
        
        left_gain = max(helper(node.left), 0)
        right_gain = max(helper(node.right), 0)
        
        price_newpath = node.val + left_gain + right_gain
        
        max_sum = max(max_sum, price_newpath)
        
        return node.val + max(left_gain, right_gain)

    helper(root)
    return max_sum

Q7. Compute the sum of values of all nodes in a binary search tree that fall within a given range [l, r].

Sample Answer: To compute the sum of values within a specified range in a binary search tree (BST), we can perform an in-order traversal while checking whether each node’s value falls within [L, R]. Here’s how you might implement it:

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

def range_sum_bst(root, L, R):
    if not root:
        return 0
    
    total_sum = 0
    
    if L <= root.val <= R:
        total_sum += root.val
        
    if root.val > L:
        total_sum += range_sum_bst(root.left, L, R)
        
    if root.val < R:
        total_sum += range_sum_bst(root.right, L, R)
        
    return total_sum

Q8. Determine the length of the longest path starting at (0,0) in a matrix.

Sample Answer: To find the longest increasing path starting from (0,0) in a matrix where movement is allowed only to adjacent cells with higher values, we can use depth-first search (DFS) with memoization to optimize performance. Here’s an example implementation:

def longest_increasing_path(matrix):
    if not matrix or not matrix[0]:
        return 0
    
    rows, cols = len(matrix), len(matrix[0])
    
    def dfs(x, y):
        if (x,y) in memo:
            return memo[(x,y)]
        
        max_length = 1
        
        for dx, dy in [(1,0), (-1,0), (0,-1), (0,+1)]:
            nx, ny = x + dx, y + dy
            
            if 0 <= nx < rows and 0 <= ny < cols and matrix[nx][ny] > matrix[x][y]:
                max_length = max(max_length, dfs(nx, ny) + 1)
        
        memo[(x,y)] = max_length
        return max_length
    
    memo = {}
    
    return dfs(0, 0)

Q9. List all strobogrammatic numbers of a given length.

Sample Answer: To generate all strobogrammatic numbers of a specified length that appear identical when rotated by 180 degrees, we can utilize recursion to build valid combinations based on pairs that form strobogrammatic digits. Here’s how this can be implemented:

def strobogrammatic_numbers(n):
    def helper(length):
        if length == 0:
            return [""]
        elif length == 1:
            return ["0", "1", "8"]
        
        prev_numbers = helper(length - 2)
        
        result = []
        
        for number in prev_numbers:
            if length != n: 
                result.append("0" + number + "0")
            result.append("1" + number + "1")
            result.append("6" + number + "9")
            result.append("8" + number + "8")
            result.append("9" + number + "6")
        
        return result
    
    return helper(n)

# Example usage for length n=2:
print(strobogrammatic_numbers(2))

Q10. Find the shortest transformation sequence from a start word to an end word.

Sample Answer: To find the shortest transformation sequence from one word to another where only one letter can change at a time and each new word must exist in a given dictionary (word list), we can use breadth-first search (BFS). This approach explores all possible transformations level by level until reaching the target word. Here’s how this can be implemented:

from collections import deque

def word_ladder_length(begin_word: str, end_word: str, word_list: set) -> int:
    queue = deque([begin_word])
    
    visited_words = {begin_word}
    
    level = 1
    
    while queue:
        for _ in range(len(queue)):
            word = queue.popleft()
            
            if word == end_word:
                return level
            
            for i in range(len(word)):
                for char in 'abcdefghijklmnopqrstuvwxyz':
                    new_word = word[:i] + char + word[i+1:]
                    
                    if new_word in word_list and new_word not in visited_words:
                        visited_words.add(new_word)
                        queue.append(new_word)

        level += 1
    
    return 0

# Example usage with sample words and dictionary.
print(word_ladder_length("hit", "cog", {"hot", "dot", "dog", "lot", "log", "cog"}))

Google Software Engineer Technical Interview Questions for Mid-Level Professionals

Mid-level software engineers are expected to bring solid practical experience and a strong grasp of core engineering concepts. At this stage, Google interviews typically focus on technical skills and how well you apply them in real-world scenarios. In this section, we’ll outline Google software interview questions and answers for mid-level candidates.

Q11. Find the minimum number of rotations needed to make all values in one row of dominoes identical. Return -1 if it’s not possible.

Sample Answer: In this problem, the goal is to determine the minimum number of rotations required to make all values in one row of dominoes identical. To solve this, we can iterate through each possible value (from 1 to 6, assuming standard dominoes) and count how many rotations would be needed to convert all dominoes in a row to that value. 

If a value cannot be achieved (i.e., if there are dominoes that do not contain that value), we return -1. The approach involves maintaining a count of each number’s occurrences and calculating the rotations needed accordingly. 

Here’s a sample implementation:

def min_rotations(dominoes):
    counts = {}
    for a, b in dominoes:
        counts[a] = counts.get(a, 0) + 1
        counts[b] = counts.get(b, 0) + 1
        
    min_rotations = float('inf')
    
    for num in counts:
        rotations = sum(1 for a, b in dominoes if a != num and b != num)
        if rotations < min_rotations:
            min_rotations = rotations
            
    return min_rotations if min_rotations != float('inf') else -1

Q12. Format a list of words into fully justified text, ensuring each line is exactly the given maximum width.

Sample Answer: Formatting text into fully justified lines requires careful management of spaces to ensure each line reaches a specified maximum width. The approach involves iterating through the words and accumulating them until adding another word would exceed the width. Once the line is full, we distribute spaces evenly between words, adding extra spaces to the leftmost gaps as necessary. 

Here’s how you might implement this:

def full_justify(words, max_width):
    result, current_line, num_of_letters = [], [], 0
    
    for word in words:
        if num_of_letters + len(word) + len(current_line) > max_width:
            for i in range(max_width - num_of_letters):
                current_line[i % (len(current_line) - 1 or 1)] += ' '
            result.append(''.join(current_line))
            current_line, num_of_letters = [], 0
        current_line.append(word)
        num_of_letters += len(word)
    
    result.append(' '.join(current_line).ljust(max_width))
    return result

Q13. Count the number of non-empty submatrices in a matrix that sum to a given target value.

Sample Answer: To count the number of non-empty submatrices that sum to a given target value in a matrix, we can utilize a hashmap to keep track of cumulative sums. By iterating through all possible pairs of rows and calculating the sum of elements between them for each column, we can check how many times each cumulative sum has been seen before. This allows us to efficiently count valid submatrices. Here’s an example implementation:

def count_submatrices(matrix, target):
    if not matrix or not matrix[0]:
        return 0
    
    count = 0
    rows, cols = len(matrix), len(matrix[0])
    
    for top in range(rows):
        sums = [0] * cols
        for bottom in range(top, rows):
            for col in range(cols):
                sums[col] += matrix[bottom][col]
                
            cum_sum_count = {0: 1}
            cum_sum = 0
            
            for s in sums:
                cum_sum += s
                count += cum_sum_count.get(cum_sum - target, 0)
                cum_sum_count[cum_sum] = cum_sum_count.get(cum_sum, 0) + 1
                
    return count

Also Read: Google Coding Interview Questions

Q14. Calculate the shortest sequence of instructions to navigate a car from position 0 with speed +1 to a target location.

Sample Answer: To achieve this, we can use breadth-first search (BFS) to explore all possible states defined by position and speed. Each state can either accelerate (double speed) or decelerate (reduce speed). By keeping track of visited states, we can efficiently find the shortest sequence of instructions needed to reach the target. Here’s an example implementation:

from collections import deque

def shortest_instructions(target):
    queue = deque([(0, 1)])  # (position, speed)
    visited = set((0, 1))
    steps = 0
    
    while queue:
        for _ in range(len(queue)):
            position, speed = queue.popleft()
            
            if position == target:
                return steps
            
            # Accelerate
            new_position = position + speed
            new_speed = speed * 2
            
            if (new_position, new_speed) not in visited:
                visited.add((new_position, new_speed))
                queue.append((new_position, new_speed))
                
            # Decelerate
            new_position = position
            new_speed = speed - 1
            
            if new_speed > 0 and (new_position, new_speed) not in visited:
                visited.add((new_position, new_speed))
                queue.append((new_position, new_speed))
        
        steps += 1
        
    return -1

Q15. Find the area of the largest rectangle containing only 1s in a binary matrix.

Sample Answer: To find the area of the largest rectangle containing only 1s in a binary matrix, we can utilize dynamic programming combined with a stack-based approach similar to finding the largest rectangle in histograms. By treating each row as a base and calculating the heights of consecutive 1s, we can compute maximum areas efficiently. Here’s an example implementation:

def maximal_rectangle(matrix):
    if not matrix or not matrix[0]:
        return 0
    
    max_area = 0
    heights = [0] * len(matrix[0])
    
    for row in matrix:
        for i in range(len(row)):
            heights[i] = heights[i] + 1 if row[i] == '1' else 0
            
        max_area = max(max_area, largest_histogram_area(heights))
        
    return max_area

def largest_histogram_area(heights):
    stack = []
    max_area = 0
    heights.append(0)
    
    for i in range(len(heights)):
        while stack and heights[stack[-1]] > heights[i]:
            h = heights[stack.pop()]
            w = i if not stack else i - stack[-1] - 1
            max_area = max(max_area, h * w)
            
        stack.append(i)
        
    return max_area

Q16. How would you build a ticketing platform?

Sample Answer: Building a ticketing platform involves several steps to ensure a seamless experience for both users and event organizers. My approach would focus on user-friendly design, robust backend architecture, and essential features for managing events effectively.

• User Experience: I would prioritize a clean and intuitive interface using React for the front end. This allows for dynamic updates and a responsive design, ensuring users can easily navigate the platform.
• Event Management Features: The platform should include functionalities for event listings, ticket purchasing, seat selection, and payment processing. Admins need tools to create and manage events efficiently.
• Backend Development: For the backend, I would use Node.js with Express for handling API requests and MongoDB as the database to store event and user data. This stack is scalable and well-suited for handling real-time data.
• Mobile Accessibility: Given the importance of mobile usage, I would develop a mobile app using Flutter to ensure a consistent user experience across devices.
• Payment Integration: Implementing secure payment gateways is crucial. I’d ensure various payment options are available, along with robust security measures to protect user data.
• Analytics and Reporting: Integrating analytics tools will help event organizers track ticket sales and user engagement, similar to Google Analytics.

Q17. How would you create a task scheduling system?

Sample Answer: Developing a task scheduling system requires careful planning to manage tasks efficiently across various resources. My approach would involve designing an architecture that supports scalability and reliability.

• System Architecture: I would use a microservices architecture to separate different functionalities like task creation, scheduling, and monitoring. This allows for easier maintenance and scaling as demand grows.
• Task Management Features: The system should enable users to create tasks with specific parameters such as deadlines, priority levels, and resource requirements. A user-friendly interface is essential for ease of use.
• Scheduling Algorithm: Implementing efficient scheduling algorithms (like round-robin or priority-based scheduling) will help allocate tasks based on resource availability. This ensures optimal utilization of resources.
• Database Selection: For data storage, I’d opt for a relational database like PostgreSQL to manage task details and user information securely.
• Notification System: Integrating notifications (via email or push notifications) will keep users informed about task statuses and upcoming deadlines.
• Monitoring Tools: Finally, incorporating monitoring tools will allow users to track the progress of their tasks in real time, ensuring transparency in the scheduling process.

Q18. Minimize the total travel distance for a group of people represented on a 2D grid, using Manhattan distance as the metric.

Sample Answer: To minimize total travel distance for a group of people represented on a 2D grid using Manhattan distance as the metric involves finding an optimal meeting point. The median coordinates provide an ideal solution since they minimize the total distance traveled. The algorithm can iterate through all coordinates to calculate distances effectively. Here’s how you might implement it:

def min_travel_distance(people_positions):
    x_coords, y_coords = zip(*people_positions)
    
    median_x = sorted(x_coords)[len(x_coords)//2]
    median_y = sorted(y_coords)[len(y_coords)//2]
    
    total_distance = sum(abs(x - median_x) + abs(y - median_y) for x, y in people_positions)
    
    return total_distance

Q19. Design an algorithm for a robot vacuum cleaner to clean an entire room modeled as a grid, using only provided movement and rotation APIs.

Sample Answer: Designing an algorithm for a robot vacuum cleaner involves creating an efficient pathfinding strategy that allows it to clean an entire room modeled as a grid. The algorithm should account for obstacles and ensure that every accessible cell is cleaned without redundancy. A common approach is using depth-first search (DFS) or breadth-first search (BFS) combined with backtracking to explore all possible paths while marking cells as cleaned. Here’s an outline for such an algorithm:

def clean_room(robot):
    def backtrack(pos, cleaned):
        cleaned.add(pos)
        robot.clean()
        
        for direction in range(4):
            next_pos = move(pos, direction)
            
            if next_pos not in cleaned and robot.move():
                backtrack(next_pos, cleaned)
                go_back() # Move back after cleaning
            
            # Rotate robot to face next direction
            
    cleaned_cells = set()
    
    backtrack((0, 0), cleaned_cells)

Q20. Find the longest path in a binary tree where all nodes have the same value. The path does not need to pass through the root.

Sample Answer: To find the longest path in a binary tree where all nodes have the same value and do not necessarily pass through the root node, we can use depth-first search (DFS). The algorithm will traverse each node while keeping track of the lengths of paths formed by nodes with identical values. Here’s how this can be implemented:

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

def longest_univalue_path(root):
    longest_path_length = [0]

    def dfs(node):
        if not node:
            return 0
        
        left_length = dfs(node.left)
        right_length = dfs(node.right)

        left_path = left_length + (node.left.val == node.val)
        right_path = right_length + (node.right.val == node.val)

        longest_path_length[0] = max(longest_path_length[0], left_path + right_path)

        return max(left_path, right_path)

    dfs(root)
    return longest_path_length[0]

Pro Tip: If you’d like to pursue a career in cybersecurity, then check out our ethical hacking course.

Google Software Engineer Interview Questions and Answers for Experienced Candidates

Experienced software engineers usually have years of knowledge and expertise. Interviews at this level are more rigorous and often include complex system design questions. As you prepare, focus on mentioning your past experiences and problem-solving strategies. Here are common Google software engineer interview questions and answers for experienced professionals.

Q21. How would you design a database for indexing web pages, similar to Google’s system?

Sample Answer: To design a database for indexing web pages, I would focus on scalability, efficiency, and quick retrieval of information. First, I would implement a distributed architecture using NoSQL databases like Cassandra or MongoDB. This allows us to handle large volumes of unstructured data effectively.

• I’d create an inverted index to facilitate fast searches based on keywords.
• A web crawler would be essential to gather data from various sites and extract relevant metadata.
• To ensure high availability and fault tolerance, I would use sharding to distribute data across multiple servers.

This approach would allow us to index and retrieve web pages efficiently, similar to what Google does.

Q22. How would you create a platform like Google Docs?

Sample Answer: Creating a collaborative document editing platform like Google Docs involves several key components. First, I would implement real-time collaboration using WebSockets, which allows multiple users to edit documents simultaneously and see changes instantly.

• For the backend, I’d use a microservices architecture with Node.js or Python, which can manage document storage and user authentication.
• I’d store documents in cloud storage solutions like AWS S3 while using a relational database like PostgreSQL for metadata.
• On the front end, I’d leverage React or Angular to create a responsive user interface.

Q23. How would you design a search engine similar to Google search?

Sample Answer: Designing a search engine requires careful consideration of crawling, indexing, and ranking algorithms. I would start with developing a robust web crawler that collects data from across the internet.

• The data collected would be indexed using an inverted index structure for quick keyword searches.
• For ranking results, I’d implement algorithms like PageRank to assess the importance of pages based on their link structures.
• Additionally, integrating machine learning models could help improve search relevance over time by analyzing user interactions with search results.

Q24. How would you create a voice assistant like Google Home?

Sample Answer: To create a voice assistant similar to Google Home, I’d focus on natural language processing (NLP) and device integration. First, I’d utilize NLP libraries such as Dialogflow to interpret user commands accurately.

• For speech recognition, I’d implement APIs like Google Speech-to-Text to convert spoken language into text.
• The assistant would need to connect with smart home devices using protocols like MQTT or HTTP APIs.
• Finally, I’d build a backend service that manages user profiles and preferences securely.

This approach would enable users to control their smart devices through voice commands effectively.

Q25. How would you design a system for previewing books like Amazon’s book previews?

Sample Answer: To design a book preview system, I’d focus on user engagement and efficient content delivery. I would adopt a microservices architecture where each service handles different functionalities—like content delivery and user management.

• Book content could be stored in cloud storage while metadata resides in a relational database.
• For the front end, I’d create an interactive UI using React that allows users to flip through pages easily.
• Implementing caching mechanisms with Redis could enhance performance by reducing load times for frequently accessed previews.

Also Read: Google Analytics Interview Questions

Q26. How would you rotate an array to the right by k steps?

Sample Answer: To rotate an array efficiently, I can use slicing. First, I can normalize k to ensure it’s within the bounds of the array length. Then, I can slice the array into two parts and concatenate them in reverse order. Here’s how I would do it:

def rotate_array(nums, k):
    n = len(nums)
    k %= n  # Normalize k
    nums[:] = nums[-k:] + nums[:-k]  # Rotate using slicing

# Example usage
array = [1, 2, 3, 4, 5, 6, 7]
rotate_array(array, 3)
print(array)  # Output: [5, 6, 7, 1, 2, 3, 4]

Q27. How would you design a system to track the number of clicks on YouTube videos?

Sample Answer: To design an analytics system for tracking video clicks, I’d focus on real-time data processing. I would capture click events using message queues like Kafka.

• These events can then be processed by worker services that aggregate click counts into databases optimized for analytics.
• Additionally, I’d develop real-time dashboards using visualization tools to present this data.

This setup allows us to track user engagement effectively and provide insights into video performance.

Q28. Can you write a function to determine if a string of parentheses is valid?

Sample Answer: Sure. A valid parentheses string must have matching opening and closing brackets in the correct order. I would use a stack to keep track of the opening brackets and ensure they match with the closing brackets. Here’s the implementation:

def is_valid_parentheses(s: str) -> bool:
    stack = []
    mapping = {')': '(', '}': '{', ']': '['}

    for char in s:
        if char in mapping:  # If it's a closing bracket
            top_element = stack.pop() if stack else '#'
            if mapping[char] != top_element:
                return False
        else:
            stack.append(char)  # It's an opening bracket

    return not stack  # If stack is empty, parentheses are valid

# Example usage
input_string = "([]){}"
print(is_valid_parentheses(input_string))  # Output: True

Q29. How would you merge overlapping intervals?

Sample Answer: To merge overlapping intervals, I would first sort the intervals based on their start times. Then, I would iterate through the sorted list and merge intervals that overlap. Here’s how I would implement this in Python:

def merge_intervals(intervals):
    if not intervals:
        return []

    # Sort the intervals by their start time
    intervals.sort(key=lambda x: x[0])
    merged = [intervals[0]]

    for current in intervals[1:]:
        last_merged = merged[-1]
        if current[0] <= last_merged[1]:  # Overlapping intervals
            last_merged[1] = max(last_merged[1], current[1])  # Merge
        else:
            merged.append(current)

    return merged

# Example usage
input_intervals = [[1, 3], [2, 6], [8, 10], [15, 18]]
print(merge_intervals(input_intervals))  # Output: [[1, 6], [8, 10], [15, 18]]

Explanation: The function sorts the intervals and then iterates through them to merge any overlapping ones. This solution runs in O(nlog⁡n) due to sorting.

Q30. Can you write a function to find the length of the longest substring without repeating characters?

Sample Answer: Sure. The problem is to determine the length of the longest substring in a given string that contains all unique characters. I would use a sliding window technique with a hash map to keep track of the characters and their indices. This approach allows us to efficiently find the longest substring while ensuring that we only traverse the string once. 

Here’s how I would implement it in Python:

def length_of_longest_substring(s: str) -> int:
    char_index_map = {}
    left_pointer = 0
    max_length = 0

    for right_pointer in range(len(s)):
        if s[right_pointer] in char_index_map:
            left_pointer = max(left_pointer, char_index_map[s[right_pointer]] + 1)
        
        char_index_map[s[right_pointer]] = right_pointer
        max_length = max(max_length, right_pointer - left_pointer + 1)

    return max_length

# Example usage
input_string = "abcabcbb"
print(length_of_longest_substring(input_string))  # Output: 3

Google Software Engineer Interview Preparation Tips

Preparing for a software engineer interview at Google can be an easy experience with the right approach and preparation. To help candidates navigate this journey, it’s essential to understand the interview process and develop effective strategies for practice. This section outlines important preparation tips that can enhance your chances of securing a job at Google.

1. Understand the Interview Process: Understanding the interview process at Google is essential for effective preparation. The process usually takes several weeks and consists of multiple stages, each designed to evaluate different skills and attributes. Here’s an overview of the Google software engineer hiring process:
   • Google Online Assessment: This initial step involves a coding test for new graduates and interns, where candidates have 90 minutes to solve two basic questions on data structures or algorithms. Passing both questions is necessary to advance.
   • Technical Screen(s): Following the online assessment, candidates participate in at least one video call with a peer or hiring manager. During this stage, you will solve coding problems on a shared Google Doc without access to autocomplete. Expect to answer basic behavioral questions as well.
   • Onsite Interviews: The onsite interview consists of 4 to 6 rounds over a single day, each lasting about 45 minutes. These rounds will cover both coding and system design questions, with more emphasis on coding challenges.
   • Behavioral Questions: Google looks for “Googleyness,” which reflects a candidate’s cultural fit. Be prepared to discuss teamwork, adaptability, and communication skills during behavioral interviews. Use the STAR technique (Situation, Task, Action, Result) to structure your responses clearly and effectively.
2. Master Data Structures and Algorithms: A major part of the interview will focus on your understanding of data structures and algorithms. Make sure to review key concepts such as arrays, linked lists, trees, graphs, hash tables, sorting algorithms, etc. 
3. Practice Coding on a Whiteboard: During onsite interviews, you may be required to solve coding problems on a whiteboard or shared document. To prepare for this format, practice coding without an IDE to simulate the interview environment. Focus on writing clean, efficient code while verbalizing your thought process to demonstrate your problem-solving approach.
4. Conduct Mock Interviews: Participating in mock interviews can provide valuable feedback and help you identify areas for improvement. Consider practicing with peers or using online platforms that offer mock interviews with experienced engineers. This practice will help you become more comfortable with the interview format and improve your communication skills.

Conclusion

Preparing for a software engineer interview at Google requires thorough preparation, given the company’s reputation for rigorous assessments and high standards. With the right approach and resources, candidates can significantly enhance their chances of success. By familiarizing yourself with different Google interview questions for software engineers and practicing coding problems, you can improve your chances of landing a job at Google. If you’re looking for tips and strategies to help you land a job at Google, check out our comprehensive guide on ‘How to get a job at Google?’

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