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Google Interview Questions (2322)

Practice real interview problems from Google

#2724

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Easy✓ Solution📹 Video
Amazon+2
#2727

Is Object Empty

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Google
#2769

Find the Maximum Achievable Number

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Amazon+3
#2784

Check if Array is Good

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Bloomberg+2
#2843

Count Symmetric Integers

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Amazon+4
#2864

Maximum Odd Binary Number

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Amazon+2
#2873

Maximum Value of an Ordered Triplet I

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Amazon+3
#2877

Create a DataFrame from List

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Amazon+4
#2878

Get the Size of a DataFrame

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Google
#2879

Display the First Three Rows

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Amazon+3
#2880

Select Data

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Bloomberg+2
#2886

Change Data Type

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Google
#2887

Fill Missing Data

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Acko+2
#2888

Reshape Data: Concatenate

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Google
#2923

Find Champion I

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Google
#2942

Find Words Containing Character

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Amazon+5
#2985Premium

Calculate Compressed Mean

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Google
#2987Premium

Find Expensive Cities

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Google
#2990Premium

Loan Types

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Google
#3000

Maximum Area of Longest Diagonal Rectangle

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Accenture+4
#3024

Type of Triangle

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Bloomberg+4
#3028

Ant on the Boundary

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Accenture+2
#3046

Split the Array

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Adobe+3
#3110

Score of a String

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Amazon+4
#3136

Valid Word

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Amazon+7
#3151

Special Array I

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Amazon+5
#3174

Clear Digits

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Amazon+4
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About Google Coding Interviews

Google’s engineering interviews are designed to test deep problem-solving ability, strong data structure knowledge, and the ability to reason through complex algorithms. Candidates are expected to write clean, efficient code while explaining their thought process clearly. Unlike some companies that emphasize memorized patterns, Google focuses on understanding fundamentals and adapting them to new problems.

The typical Google coding interview evaluates candidates on core computer science topics such as arrays, graphs, dynamic programming, trees, and advanced algorithmic thinking. Interviewers often start with a straightforward problem and progressively add constraints or follow-up variations to test how well you optimize solutions. Communication, clarity, and reasoning are just as important as the final code.

Across our dataset of 2214 real Google interview questions, several patterns appear frequently:

  • Graph traversal problems (BFS, DFS, shortest paths)
  • Dynamic programming and optimization problems
  • Tree and binary search tree manipulation
  • Advanced string and hashing problems
  • Greedy algorithms and interval problems

Google interview questions often range from medium to hard difficulty, with many requiring careful edge-case handling and time-space tradeoff analysis.

FleetCode helps you prepare efficiently by organizing real Google coding interview questions by difficulty, topic, and interview frequency. Instead of solving random problems, you can focus on patterns that actually appear in Google interviews. Each problem includes optimized solutions in Python, Java, and C++, allowing you to build the skills needed to succeed in phone screens and onsite technical rounds.

Interview Tips for Google

The Google interview process typically consists of multiple stages designed to evaluate both technical depth and problem-solving clarity. While the exact process can vary by role and location, most software engineering candidates go through the following steps.

  • Recruiter screen: A short conversation about your background, experience, and role expectations.
  • Technical phone screen (1–2 rounds): 45–60 minute coding interviews conducted in a shared editor. You will solve algorithmic problems while explaining your reasoning.
  • Onsite or virtual onsite (3–5 rounds): Multiple coding interviews plus a possible system design round for experienced candidates.
  • Hiring committee review: Interview feedback is reviewed independently before a final decision.

Common Google coding interview topics include:

  • Graphs and BFS/DFS traversal
  • Dynamic programming and recursion
  • Binary trees and tree traversal
  • Hash maps and string manipulation
  • Binary search and optimization techniques
  • Greedy algorithms and interval scheduling

Google interviewers care deeply about how you think. Always start by clarifying the problem, discussing brute-force ideas, and then improving the solution step by step. Writing perfect code immediately is less important than demonstrating structured reasoning.

Common mistakes candidates make:

  • Jumping straight into coding without discussing approach
  • Ignoring edge cases such as empty inputs or duplicates
  • Not analyzing time and space complexity
  • Failing to test the solution with sample inputs

A strong preparation strategy is to practice problems by pattern. Many successful candidates solve 200–400 high-quality DSA problems focusing on graphs, trees, and dynamic programming. Allocate about 8–12 weeks of consistent preparation, practicing daily and reviewing optimized solutions.

Using FleetCode’s curated list of 2214 Google interview questions, you can focus on problems that have actually appeared in interviews and systematically master the patterns Google evaluates most often.