Google

Google placement papers, interview questions, placement process, eligibility criteria, and complete preparation guide for 2025-2026 campus recruitment and hiring.

Google is a multinational technology company focusing on search engine technology, online advertising, cloud computing, computer software, quantum computing, e-commerce, artificial intelligence, and consumer electronics. Founded in 1998 by Larry Page and Sergey Brin, Google is now a subsidiary of Alphabet Inc.

Headquarters: Mountain View, CA
Employees: 180,000+ globally

Industry: Technology, Software
Revenue: $300+ Billion USD (2023)

Google Eligibility Criteria

Academic Requirements

Minimum Percentage: 60% throughout in 10th, 12th, and graduation (some roles may require higher)

Degree: B.Tech/B.E./M.Tech in any engineering discipline

Year of Study: Final year students and recent graduates (within 2 years)

Backlogs: No active backlogs at the time of application

Branch Eligibility

Preferred: Computer Science, Information Technology, Electronics

Also Eligible: All other engineering branches with strong programming skills

Non-Tech Roles: Business, Economics, MBA candidates for PM/Business roles

Additional Criteria

Programming Skills: Strong skills in at least one language (Python, Java, C++)

Experience: Previous internship experience (preferred but not mandatory)

Projects: Open source contributions or personal projects (highly valued)

Communication: Strong communication skills in English

Google Placement Papers

Google Placement Papers 2024

Download PDF →

Google Placement Papers 2025

Download PDF →

Google Placement Process for 2025

Application Method

Campus Recruitment

Primary Method - Through college placement cells. Pre-placement talks followed by online assessments

Online Applications

Direct Apply - Via Google Careers portal

Open throughout the year for experienced roles

Referrals

Employee Referrals - Higher success rate

Internal recommendations from Google employees

Rounds and Format

Resume Screening (1-2 weeks)

Initial screening based on academic performance, projects, and technical skills. Focus on:
- Academic credentials and consistency
- Relevant project experience and internships
- Programming contest participation (ACM ICPC, CodeChef, etc.)
- Open source contributions and GitHub profile
Online Assessment - 90 minutes

Format: Conducted on Google’s internal platform
- Coding Problems: 2 algorithmic problems (medium difficulty)
- Multiple Choice: 15-20 questions on CS fundamentals
- Topics Covered: Data structures, algorithms, time complexity, basic system design
- Passing Criteria: Both coding problems must be solved with optimal time complexity
Success Rate: Approximately 15-20% candidates advance to interview rounds
Technical Interview Round 1 - 45-60 minutes

Interviewer: Senior Software Engineer or Staff Engineer
- Coding Focus: 1-2 algorithmic problems on Google Docs or whiteboard
- Difficulty: Medium level, focus on arrays, strings, trees, graphs
- Discussion Points: Code optimization, edge case handling, testing approach
- Evaluation: Problem-solving approach, coding style, communication skills
Technical Interview Round 2 - 45-60 minutes

Interviewer: Senior Engineer or Engineering Manager
- Advanced Coding: Complex algorithmic problems, system design basics
- Project Discussion: Deep dive into candidate’s best technical project
- Scalability Questions: How would you handle 1 million users?
- Past Experience: Internships, hackathons, technical challenges faced
Behavioral Interview - 30-45 minutes (Googleyness)

Interviewer: Engineering Manager or Senior Staff Engineer
- Leadership Assessment: Examples of leading technical projects or teams
- Problem Solving: How you approach ambiguous or challenging situations
- Cultural Fit: Alignment with Google’s values and work culture
- Learning Agility: Examples of quickly learning new technologies or domains
Final Committee Review - 1 week

Hiring Committee: Panel of senior engineers and managers
- All interview feedback is compiled and reviewed
- Decision based on technical skills, cultural fit, and growth potential
- Background verification and reference checks
- Offer letter preparation with role and compensation details

Timeline from Application to Offer

Phase	Duration	Key Activities
Resume Screening	1-2 weeks	Initial evaluation and shortlisting
Online Assessment	1 day	Scheduled coding test with immediate scoring
Interview Scheduling	3-5 days	Coordination for technical and behavioral rounds
Technical Rounds	1-2 weeks	Two technical interviews spaced 2-3 days apart
Behavioral Round	3-5 days	Usually scheduled after technical rounds
Committee Review	5-7 days	Final decision and offer preparation
Offer Communication	1-2 days	Offer letter with joining details

Sample Questions

Coding Problems

Two Sum

Given array of integers and target, return indices of two numbers that add up to target.

Example: [2,7,11,15], target=9 → [0,1]

def twoSum(nums, target):
    lookup = {}
    for i, num in enumerate(nums):
        if target - num in lookup:
            return [lookup[target - num], i]
        lookup[num] = i

Valid Parentheses

Check if string with brackets is valid.

Example: ”()[]” → true, ”([)]” → false

def isValid(s):
    stack = []
    mapping = {')': '(', '}': '{', ']': '['}
    for char in s:
        if char in mapping.values():
            stack.append(char)
        elif char in mapping:
            if not stack or stack.pop() != mapping[char]:
                return False
        else:
            return False
    return not stack

Merge Two Sorted Lists

Merge two sorted linked lists.

Example: [1,2,4] + [1,3,4] → [1,1,2,3,4,4]

class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next
def mergeTwoLists(l1, l2):
    dummy = ListNode()
    curr = dummy
    while l1 and l2:
        if l1.val < l2.val:
            curr.next = l1
            l1 = l1.next
        else:
            curr.next = l2
            l2 = l2.next
        curr = curr.next
    curr.next = l1 or l2
    return dummy.next

Add Two Numbers (Linked List)

Add numbers represented as linked lists.

Example: (2→4→3) + (5→6→4) = (7→0→8) Represents: 342 + 465 = 807

class ListNode:
    def __init__(self, val=0, next=None):
        self.val = val
        self.next = next
def addTwoNumbers(l1, l2):
    dummy = ListNode()
    curr = dummy
    carry = 0
    while l1 or l2 or carry:
        v1 = l1.val if l1 else 0
        v2 = l2.val if l2 else 0
        val = v1 + v2 + carry
        carry = val // 10
        curr.next = ListNode(val % 10)
        curr = curr.next
        l1 = l1.next if l1 else None
        l2 = l2.next if l2 else None
    return dummy.next

Longest Substring Without Repeating

Find length of longest substring without repeating chars.

Example: “abcabcbb” → 3 (substring “abc”)

def lengthOfLongestSubstring(s):
    seen = {}
    left = 0
    max_len = 0
    for right, char in enumerate(s):
        if char in seen and seen[char] >= left:
            left = seen[char] + 1
        seen[char] = right
        max_len = max(max_len, right - left + 1)
    return max_len

Binary Tree Level Order Traversal

Return level order traversal of binary tree.

Example: [3,9,20,null,null,15,7] → [[3],[9,20],[15,7]]

from collections import deque
class TreeNode:
    def __init__(self, val=0, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right
def levelOrder(root):
    if not root:
        return []
    result, queue = [], deque([root])
    while queue:
        level = []
        for _ in range(len(queue)):
            node = queue.popleft()
            level.append(node.val)
            if node.left:
                queue.append(node.left)
            if node.right:
                queue.append(node.right)
        result.append(level)
    return result

Median of Two Sorted Arrays

Find median with O(log(min(m,n))) complexity.

Example: [1,3], [2] → 2.0

def findMedianSortedArrays(nums1, nums2):
    A, B = nums1, nums2
    m, n = len(A), len(B)
    if m > n:
        A, B, m, n = B, A, n, m
    imin, imax, half = 0, m, (m + n + 1) // 2
    while imin <= imax:
        i = (imin + imax) // 2
        j = half - i
        if i < m and B[j-1] > A[i]:
            imin = i + 1
        elif i > 0 and A[i-1] > B[j]:
            imax = i - 1
        else:
            if i == 0: max_of_left = B[j-1]
            elif j == 0: max_of_left = A[i-1]
            else: max_of_left = max(A[i-1], B[j-1])
            if (m + n) % 2 == 1:
                return max_of_left
            if i == m: min_of_right = B[j]
            elif j == n: min_of_right = A[i]
            else: min_of_right = min(A[i], B[j])
            return (max_of_left + min_of_right) / 2.0

Serialize/Deserialize Binary Tree

Design algorithm to serialize and deserialize binary tree. Choose your format but ensure it works for any tree structure.

class Codec:
    def serialize(self, root):
        vals = []
        def dfs(node):
            if not node:
                vals.append('#')
                return
            vals.append(str(node.val))
            dfs(node.left)
            dfs(node.right)
        dfs(root)
        return ' '.join(vals)
    def deserialize(self, data):
        vals = iter(data.split())
        def dfs():
            val = next(vals)
            if val == '#':
                return None
            node = TreeNode(int(val))
            node.left = dfs()
            node.right = dfs()
            return node
        return dfs()

Edit Distance

Find minimum operations to convert one string to another (insert, delete, replace).

def minDistance(word1, word2):
    m, n = len(word1), len(word2)
    dp = [[0]*(n+1) for _ in range(m+1)]
    for i in range(m+1):
        for j in range(n+1):
            if i == 0:
                dp[i][j] = j
            elif j == 0:
                dp[i][j] = i
            elif word1[i-1] == word2[j-1]:
                dp[i][j] = dp[i-1][j-1]
            else:
                dp[i][j] = 1 + min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1])
    return dp[m][n]

Practice More Google Interview Questions →

Technical Concepts

Core Data Structures

Arrays & Strings: Two pointers, sliding window, prefix sums

Linked Lists: Reversal, cycle detection, merging

Trees & Graphs: BFS/DFS, tree traversals, shortest paths

Hash Tables: Design, collision handling, time complexity

Heaps: Priority queues, heap operations, k-way merge

Dynamic Programming: Memoization, tabulation, optimization

Algorithms (High Priority)

Sorting: Quick sort, merge sort, counting sort applications

Searching: Binary search variants, two pointers technique

Graph Algorithms: Dijkstra, Union-Find, topological sorting

Tree Algorithms: LCA, tree DP, BST operations

String Algorithms: Pattern matching, suffix arrays

System Design Basics

Scalability: Horizontal vs vertical scaling strategies

Load Balancing: Round-robin, consistent hashing

Databases: SQL vs NoSQL, ACID properties, sharding

Caching: Redis, Memcached, cache eviction policies

APIs: RESTful design, rate limiting, authentication

CS Fundamentals

Operating Systems: Process management, memory, concurrency

Computer Networks: TCP/IP, HTTP/HTTPS, DNS, CDN

Database Management: Indexing, query optimization, transactions

Software Engineering: OOP principles, design patterns, testing

Behavioral Questions

“Tell me about a time you led a technical project”

Use STAR method: Situation, Task, Action, Result

Project scope and your leadership role
Challenges and decision-making process
Team coordination and conflict resolution
Quantified impact and learning outcomes

“Difficult teammate situation”

Focus on collaboration and problem-solving approach

Preparation Strategy

Data Structures & Algorithms

Priority: Critical

Time Allocation: 60% of preparation time

Master arrays, trees, graphs, and dynamic programming
Practice 300+ LeetCode problems (focus on medium difficulty)
Understand time/space complexity analysis thoroughly

System Design

Priority: High (L4+ roles)

Time Allocation: 20% of preparation time

Learn scalability concepts and distributed systems basics
Practice designing popular systems (URL shortener, chat app)
Understand database design and API architecture

Behavioral Interview

Priority: High

Time Allocation: 15% of preparation time

Prepare 8-10 STAR format stories covering different scenarios
Research Google’s culture and values thoroughly
Practice articulating technical projects clearly

CS Fundamentals

Priority: Medium

Time Allocation: 5% of preparation time

Review operating systems, networks, and database concepts
Focus on concepts that relate to system design
Understand basic security and performance principles

Recommended Study Plan

Foundation Building Phase

Complete data structures and algorithms fundamentals
Start solving easy and medium LeetCode problems daily (aim for 2-3 problems)
Begin system design learning with basic concepts
Read “Cracking the Coding Interview” thoroughly
Set up coding practice routine and track progress
Join competitive programming platforms (CodeChef, Codeforces)

Compensation & Benefits (India)

Software Engineer Roles

Level	Experience	Base Salary	Total Package	Typical Background
L3	New Grad	₹18-25 LPA	₹30-45 LPA	Fresh graduates, top-tier colleges
L4	0-2 years	₹25-35 LPA	₹45-70 LPA	1-2 years experience, strong performers
L5	3-5 years	₹40-60 LPA	₹70-120 LPA	Senior engineers, tech leads
L6	6+ years	₹70-100 LPA	₹1 Cr+	Staff engineers, managers

Other Popular Roles

Role	Level	Total Package	Requirements
Product Manager	APM/PM	₹35-60 LPA	MBA preferred, product sense
Data Scientist	L4-L5	₹40-80 LPA	ML/AI expertise, PhD preferred
Site Reliability Engineer	L4-L5	₹45-85 LPA	DevOps, distributed systems
UX Designer	UX3-UX4	₹30-55 LPA	Design portfolio, user research

Benefits & Perks

Stock Options: 4-year vesting schedule with annual refreshers
Health Insurance: Comprehensive coverage for employee and family
Food & Transportation: Free meals, shuttle services in major cities
Learning Budget: ₹1.5 LPA for courses, conferences, certifications
Flexible Work: Hybrid work options, flexible hours
Parental Leave: Up to 6 months paid parental leave
Relocation Support: Assistance for internal transfers and new hires

Recent Updates

Hiring Trends 2025

System Design Focus: Even L3 roles now include basic system design questions

AI/ML Emphasis: Growing demand for candidates with machine learning and AI experience

Product Sense: Technical roles increasingly require understanding of product impact

Open Source Contributions: Higher weightage given to GitHub profiles and contributions

Process Changes

Extended Interview Process: Additional behavioral round for cultural fit assessment

Pair Programming: Some teams now include collaborative coding sessions

Take-Home Assignments: For certain specialized roles, 2-3 day coding challenges

Continuous Evaluation: Multiple checkpoints instead of single elimination rounds

New Initiatives

Campus Ambassador Program: Enhanced university partnerships for early talent identification

Diversity Hiring: Increased focus on recruiting from diverse backgrounds and colleges

Remote-First Roles: More positions available for remote work from tier-2/3 cities

Internship to Full-Time: Higher conversion rates from internship programs

Competition & Market

Salary Inflation: 15-20% increase in compensation packages compared to 2024

Faster Decision Making: Reduced time-to-hire to compete with other tech giants

Flexible Joining: Multiple joining dates throughout the year instead of fixed cycles

Counter-Offer Strategy: Competitive packages to retain talent against other FAANG companies

Ready to start your Google preparation? Focus on data structures and algorithms first, then gradually build system design and behavioral interview skills. Remember, consistency in practice is more important than intensive cramming.

Pro Tip: Join Google developer communities, attend tech talks, and contribute to open source projects to build your profile beyon