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Google Coding Sample 90 Minutes


In the dynamic landscape of technical interviews, Google stands out with its demanding coding assessments. The Google Coding Sample, a succinct 90-minute evaluation, is strategically crafted to gauge your problem-solving acumen and coding dexterity. In this comprehensive guide, we will delve into the intricacies of what to anticipate and offer valuable tips to bolster your chances of success.

Decoding the Google Coding Sample:

The Google Coding Sample typically comprises an array of algorithmic problems, carefully curated to assess your proficiency in constructing efficient and accurate code. Expect challenges that delve into data structures, algorithms, and advanced problem-solving techniques.

Strategic Preparation Tips:

  1. Core Concept Review: Initiate your preparation by revisiting and reinforcing fundamental concepts in data structures and algorithms. Ensure a solid understanding of key structures like arrays, linked lists, trees, and graphs, as well as foundational algorithms such as sorting and searching.

  2. Regular and Varied Practice: Embrace a consistent practice routine on platforms like LeetCode and HackerRank. Engage with problems of varying difficulty levels to broaden your problem-solving skills. Regular practice fosters familiarity with different patterns and strategies for approaching diverse coding challenges.

  3. Time Management Mastery: Acknowledge and refine your time management skills. Given the time-constrained nature of the Google Coding Sample (90 minutes), practice solving problems within this specific timeframe. Develop a sense of pacing to ensure efficiency during the actual assessment.

  4. Concept Application: Move beyond rote memorization by applying core concepts to practical coding scenarios. Understand how to adapt and implement these concepts in problem-solving, recognizing their real-world relevance. Practical application solidifies your conceptual understanding.

  5. Learn from Mistakes: Embrace a growth mindset and view mistakes as learning opportunities. Analyze and understand errors made during practice sessions. Identify patterns in your mistakes and actively work towards avoiding them in subsequent attempts, fostering continuous improvement.

  6. Mock Assessments: Simulate the actual assessment conditions by undertaking mock coding assessments. Practice under time constraints and diverse problem scenarios to acclimate yourself to the testing environment. Mock assessments provide valuable insights into areas that may require additional focus.

  7. Resource Utilization: Leverage a variety of learning resources. Utilize online tutorials, textbooks, and coding forums to gain diverse perspectives on problem-solving approaches. Expanding your resource pool enhances your understanding and exposure to different coding techniques.

  8. Peer Collaboration: Engage in collaborative learning by discussing coding problems with peers. Sharing insights, discussing strategies, and reviewing each other’s solutions can provide fresh perspectives and deepen your understanding of various problem-solving approaches.

  9. Documentation of Learnings: Keep a record of your learnings and insights during your preparation. Documenting your approach to different types of problems and the lessons learned from each session creates a personalized reference guide that you can revisit for reinforcement.

  10. Balanced Approach: Strike a balance between breadth and depth in your preparation. Cover a variety of coding topics while also delving deeply into specific areas of interest or weakness. A balanced approach ensures a well-rounded skill set.

Illustrative Coding Problem:

Consider the following problem: Given an array of integers, find two numbers that sum up to a specific target.

					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 None


In this example, the two_sum function employs a dictionary (seen) to store the complement of each number as the array is traversed. The function then returns the indices of the two numbers when a number’s complement is found in the dictionary.

Essential Considerations:

    1. Algorithmic Efficiency: Consider the efficiency of the algorithm you choose to solve the problem. Aim for solutions that have optimal time and space complexity. Assess the scalability of your solution to ensure it performs well, especially with larger datasets.

    2. Data Structure Selection: Choose appropriate data structures based on the requirements of the problem. Optimal data structure selection can significantly impact the efficiency of your solution. Consider factors like lookup time, insertion/deletion complexity, and overall suitability for the problem.

    3. Edge Case Handling: Pay special attention to edge cases—situations where the input may deviate from the typical or expected values. Ensure that your solution accounts for these scenarios to guarantee the correctness and reliability of your code.

    4. Code Readability and Maintainability: Craft code that is easy to read, understand, and maintain. Using descriptive variable names and organizing your code logically can enhance its readability. A well-structured and comprehensible codebase facilitates collaboration and future modifications.

    5. Error Handling: Implement robust error-handling mechanisms to anticipate and manage potential issues. Providing informative error messages and handling exceptional cases gracefully contributes to the overall reliability of your solution.

    6. Time and Space Trade-offs: Recognize the trade-offs between time and space complexity in your solution. Depending on the problem requirements, it might be necessary to prioritize one over the other. Striking a balance between these factors is crucial for crafting an efficient solution.

    7. Scalability: Consider how well your solution scales with increasing input sizes. Solutions that can handle varying levels of data without a significant degradation in performance are generally more desirable, especially in real-world applications.

    8. Test Coverage: Thoroughly test your code with diverse sets of test cases. Ensure that your solution works correctly across different scenarios, covering both typical and edge cases. Comprehensive testing enhances the robustness of your code.

    9. Code Optimization: Look for opportunities to optimize your code without sacrificing correctness. Consider refining your algorithm or making small adjustments to improve its efficiency. However, prioritize clarity and maintainability over excessive optimization unless necessary.

By carefully considering these factors, you contribute to the development of a well-rounded and effective solution. These essential considerations go beyond mere code implementation and encompass the broader aspects of crafting high-quality and reliable code for solving coding problems.

Concluding Insights:

Preparing for the Google Coding Sample necessitates a delicate balance of foundational comprehension, consistent practice, and astute time management. By acquainting yourself with prevalent coding patterns and embracing regular practice, you equip yourself to navigate the challenges presented in the 90-minute assessment with confidence. Best of luck on your coding journey!


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