Top 10 Software Engineering Interview Questions Asked at Google

Securing a job at Google is a dream for many software engineers, but getting through their rigorous interview process can be a daunting task. Google is known for its challenging technical interviews that test your problem-solving abilities, coding skills, and understanding of system design. In this article, we will delve into the top 10 software engineering interview questions typically asked at Google and how you can prepare for them.

1. What is a Binary Search Tree? How is it different from a Binary Tree?

A Binary Search Tree (BST) is a type of binary tree where the left subtree contains nodes with values less than the root, and the right subtree contains nodes with values greater than the root. This property makes searching, inserting, and deleting nodes much more efficient.

Key Differences Between Binary Tree and Binary Search Tree:

  • In a binary tree, there is no specific order of nodes, while in a BST, there is an inherent order.
  • Searching in a binary tree may require traversing all nodes, but in a BST, the search operation is much faster due to the order.

Advantages of BST:

  • Faster search times (O(log n) in a balanced tree).
  • Efficient insertion and deletion operations.

Example:

A Binary Tree can look like this:

  10

 /  \

5   15

   /  \

  12  20

  •  

A Binary Search Tree can look like this:

  10

 /  \

5   15

    /  

   12  

 

Topics for Further Reading:

  • Apple System Design Questions
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Recommended Topic: Apple System Design Questions

2. Explain the concept of Dynamic Programming (DP). Can you solve a problem using DP?

Dynamic Programming is an optimization technique used to solve problems by breaking them down into simpler subproblems and storing the solutions to those subproblems to avoid recomputation. DP is particularly useful in solving problems that involve overlapping subproblems.

Steps to Solve DP Problems:

  1. Define the state: Identify the subproblems.
  2. Recurrence Relation: Find a relationship between subproblems.
  3. Base Cases: Define base cases to terminate the recursion.
  4. Memoization or Tabulation: Use either top-down (memoization) or bottom-up (tabulation) approach to store the results.

Example Problem:
Fibonacci Sequence

Recursive Solution:

fib(n) = fib(n-1) + fib(n-2)

 

  • DP Solution: Store results of fib(n-1) and fib(n-2) to avoid recomputation.

Advantages of DP:

  • Solves complex problems efficiently.
  • Avoids redundant calculations, leading to time savings.

 

Also Read: How to Ace System Design Interviews

3. Describe the differences between a HashMap and a HashTable. When would you use each?

3. Describe the differences between a HashMap and a HashTable. When would you use each

A HashMap and a HashTable are both data structures used to store key-value pairs, but they have some crucial differences in terms of synchronization, performance, and usage.

Key Differences:

  • Synchronization: HashMap is not synchronized, meaning it is not thread-safe, whereas HashTable is synchronized and thread-safe.
  • Performance: Since HashMap is not synchronized, it tends to perform better in single-threaded environments compared to HashTable.
  • Null Keys/Values: HashMap allows one null key and multiple null values, while HashTable does not allow null keys or null values.

When to Use:

  • Use HashMap when thread-safety is not a concern, and you need high-performance operations.
  • Use HashTable when you need synchronization and thread-safety, but with a tradeoff in performance.

Example:

HashMap:


HashMap<String, Integer> map = new HashMap<>();

map.put(“A”, 1);

map.put(“B”, 2);

HashTable:

Hashtable<String, Integer> table = new Hashtable<>();

table.put(“A”, 1);

table.put(“B”, 2);

 

Topics for Further Reading:

  • 20 Must-Know System Design Questions
  • 20 API Design Questions for System Design Interviews

Recommended Topic: Critical System Design Questions for Tech Interviews

4. How would you design a URL shortening service (like bit.ly)?

Designing a URL shortening service is a classic system design problem. The main goal is to map a long URL to a short, unique string that can be used to redirect users to the original URL.

Key Considerations:

  • Uniqueness: Ensure that each short URL is unique to prevent collisions.
  • Scalability: The system should handle millions of URLs and shorten them efficiently.
  • Redirection: The service should provide a quick redirect to the original URL when the short URL is accessed.
  • Persistence: Store the mapping between the short and original URL persistently.

High-Level Design:

  • Use a hashing algorithm to generate the short URL.
  • Store the mapping in a database for quick retrieval.
  • Implement caching to handle frequently accessed URLs and reduce latency.

Topics for Further Reading:

  • Critical System Design Questions for Tech Interviews
  • Uber System Design Questions

Also Read: Airbnb System Design Questions

5. What is the difference between a process and a thread? How do they interact?

A process is an independent program in execution, while a thread is a unit of execution within a process. Multiple threads can exist within a single process and share the same memory space.

Key Differences:

  • Memory: Processes have separate memory spaces, whereas threads share the same memory within a process.
  • Execution: Processes execute independently, but threads are dependent on their parent process.

Interaction:

  • Threads within the same process can communicate with each other more easily, while inter-process communication (IPC) is required for communication between different processes.
  • Threads can also cause issues like race conditions if they are not properly synchronized.

Example:

  • A process might be running a web server.
  • The threads inside the server handle incoming requests concurrently.

Topics for Further Reading:

  • How to Ace System Design Interviews
  • What Interviewers Look for in System Design

6. Explain the concept of Big O notation. How do you calculate the time complexity of an algorithm?

6 Explain the concept of Big O notation. How do you calculate the time complexity of an algorithm

Big O notation is used to describe the performance or complexity of an algorithm in terms of time or space. It provides an upper bound on the growth of an algorithm’s run-time or memory usage as the input size increases.

Key Concepts:

  • Time Complexity: Measures the amount of time an algorithm takes to run as a function of the input size.
  • Space Complexity: Measures the amount of memory an algorithm uses as a function of the input size.

Common Big O Notations:

  • O(1): Constant time.
  • O(log n): Logarithmic time (e.g., binary search).
  • O(n): Linear time (e.g., linear search).
  • O(n²): Quadratic time (e.g., bubble sort).

Example:

  • Linear Search: O(n)
  • Binary Search: O(log n)

Topics for Further Reading:

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What is a Deadlock? How do you prevent or resolve it?

A deadlock is a situation in which two or more processes are unable to proceed because they are each waiting for the other to release a resource. It can lead to system crashes or performance issues.

Deadlock Prevention:

  1. Resource Allocation: Ensure that resources are allocated in a way that prevents circular dependencies.
  2. Timeouts: Implement timeouts to break potential deadlocks by forcefully releasing resources after a certain period.
  3. Lock Ordering: Enforce a strict order in which locks are acquired to avoid circular dependencies.

Deadlock Resolution:

  • If a deadlock is detected, terminate one or more of the processes involved and release the resources.

Topics for Further Reading:

  • 20 Best Software Frameworks for 2025
  • Shopify System Design Questions

Also Read: Mobile App System Design Questions

8. Explain the difference between deep copy and shallow copy in programming.

A deep copy creates a new object and recursively copies all objects, while a shallow copy only copies the reference to the objects.

Key Differences:

  • Shallow Copy: Copies references to the objects, meaning changes to the objects in the copied list will affect the original.
  • Deep Copy: Creates a completely new copy of the objects, so changes in the copied list do not affect the original.

Example:

Shallow Copy:


original = [1, 2, [3, 4]]

shallow_copy = original.copy()

 

Deep Copy:

import copy

original = [1, 2, [3, 4]]

deep_copy = copy.deepcopy(original)



9. How would you optimize a database query?

Optimizing database queries is crucial for improving performance, especially when dealing with large datasets.

Key Optimization Techniques:

  • Indexing: Create indexes on columns that are frequently queried.
  • Query Refactoring: Rewrite queries to avoid unnecessary joins or subqueries.
  • Database Partitioning: Split large tables into smaller, more manageable pieces.

Example:

  • Use of EXPLAIN command in SQL to analyze the execution plan and optimize the query.

10. How do you handle version control in large-scale software development?

10. How do you handle version control in large-scale software development?

Version control is essential for managing changes to code and collaborating effectively in large teams. Git is one of the most popular version control systems.

Key Version Control Practices:

  • Branching: Use branches to manage new features or bug fixes.
  • Commit Messages: Write clear, concise commit messages to describe changes.
  • Merge Requests: Use pull requests for code reviews before merging changes.

Example:

Using Git for version control:
git checkout -b new-featuregit commit -m “Added new feature”

 

Topics for Further Reading:

  • Critical System Design Questions for Tech Interviews
  • 20 API Design Questions for System Design Interviews

 

FAQs

What is the importance of Big O notation in interviews?

Big O notation is crucial in interviews because it helps interviewers assess a candidate’s ability to analyze the efficiency of an algorithm. Understanding Big O allows you to write optimized code, which is critical when working on large-scale systems.

How can I prepare for system design interviews at Google?

Preparing for system design interviews at Google requires practice on real-world problems, understanding the key components of system architecture, and being able to explain your design choices clearly. It is recommended to study Apple System Design Questions, Airbnb System Design Questions, and 20 API Design Questions for System Design Interviews to get a feel of what to expect.

What kind of algorithms should I know for Google interviews?

For Google interviews, you should be proficient in common algorithms like binary search, dynamic programming, graph algorithms, and sorting algorithms. Being comfortable with data structures like hashmaps, arrays, and binary trees is essential.

This comprehensive guide to Google’s software engineering interview questions equips you with the knowledge needed to prepare effectively. For more advanced system design preparation, explore topics like Uber System Design Questions and What Interviewers Look for in System Design.

Key Concept Explanation Examples
Continuity Features
Ensures users can switch between devices effortlessly.
Handoff, Universal Clipboard
Unified OS Frameworks
Streamlined frameworks like SwiftUI enable consistent UI design across devices.
iOS, macOS, iPadOS integration
Integrated Cloud Services
Centralized data management across devices through cloud solutions.
iCloud, Apple ID

These principles not only elevate the user experience but also strengthen brand loyalty, positioning Apple as a leader in consumer technology.

How to Approach Scalable Cloud Services in Apple System Design Interviews

How to Approach Scalable Cloud Services in Apple System Design Interviews

Cloud services form the backbone of Apple’s vast ecosystem, supporting scalable and reliable operations for billions of users worldwide. When discussing scalable cloud services in Apple System Design interviews, focus on key architectural principles and performance optimization techniques.

 

Core Elements of Scalable Cloud Services

 

  1. Distributed Systems
    Apple employs distributed systems to ensure fault tolerance and load balancing. Understanding the architecture of distributed databases and microservices is essential.

     

  2. Data Consistency
    Achieving consistency across Apple’s global data centers is crucial. Techniques like eventual consistency and replication strategies are key areas of focus.

     

  3. Scalability and Performance
    Apple’s cloud infrastructure must dynamically scale to handle fluctuating user demands, particularly during product launches or updates.

    Strategies for Scalable Cloud Design
Focus Area Approach Real-World Example
Load Balancing
Use load balancers to distribute traffic evenly across servers.
Apple Music streaming
Database Sharding
Partition databases to enhance query performance and scalability.
App Store transactions
Caching Mechanisms
Reduce latency by caching frequently accessed data.
iCloud data retrieval


Challenges in Hardware-Software Integration and How Apple Tackles Them

 

Hardware-software integration is a critical challenge that Apple navigates with precision. Its ability to create tightly integrated systems like the iPhone and Mac is a testament to its innovative strategies.

 

Common Challenges in Hardware-Software Integration

 

  1. Latency and Performance Issues
    Ensuring smooth communication between hardware components and software applications requires robust optimization techniques.
  2. Power Efficiency
    Apple focuses on hardware-software co-design to improve power efficiency, extending battery life without compromising performance.
  3. Hardware Compatibility
    Supporting legacy hardware while introducing cutting-edge features poses a significant challenge for Apple’s engineering teams.

     

Apple’s Solutions to Integration Challenges

Challenge Apple’s Approach Real-World Example
Latency Optimization
Implements custom chipsets like the M1 for seamless hardware-software communication.
Apple Silicon processors
Power Efficiency
Integrates power-saving algorithms at both hardware and OS levels.
macOS battery optimization
Legacy Support
Ensures backward compatibility through adaptive OS features.
iOS updates for older iPhone models

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