Skip to content

Google Placement Papers 2025 - Latest Questions & Solutions

Download latest Google placement papers 2025 PDF with current year online assessment questions, solutions, and updated exam patterns for 2025 recruitment.

This page contains Google placement papers from 2025 with current year questions, solutions, and exam patterns. Use these papers to understand the latest 2025 online assessment pattern and prepare effectively for ongoing Google recruitment drives.

Google Online Assessment 2025 Exam Pattern

Section titled “Google Online Assessment 2025 Exam Pattern”
SectionQuestionsTimeDifficultyFocus Areas
Coding Problem 1130 minMediumArrays, strings, two pointers
Coding Problem 2130 minHardTrees, graphs, dynamic programming
CS Fundamentals MCQs18-2030 minMediumDSA, time complexity, system design, AI/ML basics

Total: 20-22 questions, 90 minutes

Key Changes in 2025:

  • AI/ML basics added to MCQs
  • Product impact understanding emphasized
  • Extended behavioral round for cultural fit
  • Faster interview-to-offer timeline (6-8 weeks)

2025 Online Assessment Questions & Solutions

Section titled “2025 Online Assessment Questions & Solutions”
Q1: Median of Two Sorted Arrays (2025)

Problem: Find the median of two sorted arrays with O(log(min(m,n))) complexity.

Example: Input: nums1 = [1,3], nums2 = [2], Output: 2.0

Solution:

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

Explanation: Binary search on smaller array. Partition both arrays such that left half has equal elements. Check partition validity and find median.

Answer: Returns median with O(log(min(m,n))) time complexity

Q2: Design Rate Limiter (2025)

Problem: Implement a rate limiter that allows at most N requests per time window T.

Solution:

from collections import deque
import time
class RateLimiter:
def __init__(self, max_requests, time_window):
self.max_requests = max_requests
self.time_window = time_window
self.requests = deque()
def is_allowed(self):
current_time = time.time()
# Remove requests outside time window
while self.requests and self.requests[0] < current_time - self.time_window:
self.requests.popleft()
if len(self.requests) < self.max_requests:
self.requests.append(current_time)
return True
return False

Explanation: Sliding window approach. Maintain deque of request timestamps. Remove old requests outside window, check if space available.

Answer: Rate limiter with O(1) average time complexity

Hiring Volume

  • Total Hires: 600+ freshers expected across India
  • Software Engineer L3: 540+ selections
  • Product Manager: 35+ selections
  • Data Scientist: 25+ selections
  • Growth: 20% increase from 2024

Salary Packages

  • Software Engineer L3: ₹32-48 LPA
  • Product Manager: ₹35-60 LPA
  • Data Scientist: ₹40-80 LPA
  • 10% increase from 2024 packages

New Initiatives

  • AI/ML knowledge emphasis
  • Product thinking assessment
  • Extended behavioral rounds
  • Remote-first opportunities

Coding Problems:

  • Dynamic programming remains dominant (35%)
  • System design implementation (30%)
  • Graph algorithms (25%)
  • Array/string manipulation (10%)

New Focus Areas:

  • AI/ML basics in MCQs
  • Product impact questions
  • Scalability thinking

Key Insights from 2025 Google Online Assessment

Section titled “Key Insights from 2025 Google Online Assessment”
  1. Coding Section is Critical: Must solve both problems correctly with optimal solutions to advance
  2. AI/ML Knowledge: Basic ML concepts now tested in online assessment MCQs
  3. Product Thinking: Technical roles require product impact understanding
  4. Faster Process: Reduced time-to-hire (6-8 weeks) to compete with other tech giants
  5. Remote Opportunities: More positions available for remote work
  6. Googleyness: Behavioral round emphasizes collaboration, innovation, and problem-solving approach
  7. Difficulty Level: Google interviews rated 3.4-3.5/5 difficulty - most challenging among FAANG
  8. Extended Behavioral: More focus on cultural fit and product impact in 2025

Based on recent candidate experiences from 2025 Google interviews:

2025 Interview Process:

  1. Online Assessment (90 minutes): 2 coding problems + 18-20 CS fundamentals MCQs (including AI/ML basics)
  2. Technical Phone Screen (45-60 minutes): Coding problems, algorithm discussions
  3. Onsite Interviews (4-5 rounds, 45 minutes each):
    • Coding rounds (2-3): Algorithms, data structures, problem-solving
    • System Design round: For experienced candidates
    • Behavioral round: Extended focus on “Googleyness” and product impact

2025 Interview Trends:

  • Increased emphasis on AI/ML knowledge even for general SWE roles
  • More product thinking questions in technical rounds
  • Extended behavioral round for better cultural fit assessment
  • Faster interview-to-offer timeline (6-8 weeks vs 8-12 weeks previously)

Common 2025 Interview Topics:

  • Coding: Graph algorithms, dynamic programming, string manipulation, tree problems
  • System Design: URL shortener, distributed systems, scalability, AI/ML systems
  • Behavioral: Collaboration examples, innovation stories, product impact
  • Product Thinking: Design products, identify competitors, improve existing products
  • AI/ML Basics: Fundamental ML concepts, neural networks basics, data preprocessing

2025 Interview Questions Examples:

  • “Design a URL Shortener” (System Design)
  • “Serialize and Deserialize Binary Tree” (Coding)
  • “Choose a product you like and explain how you would identify its competitors” (Product)
  • “Design a voice assistant product for kids” (Product Design)

Success Tips:

  • Strong coding performance is essential - solve problems optimally with clean code
  • Practice explaining your thought process clearly
  • Prepare examples demonstrating collaboration, innovation, and product impact
  • Focus on “Googleyness” - show how you think about problems and user impact
  • Learn AI/ML basics even for general SWE roles
  • Be ready for extended behavioral questions about product thinking

Difficulty Rating: 3.4-3.5/5 (Most challenging among FAANG)

For detailed interview experiences from 2025, visit Google Interview Experience page.

  1. Master Coding Fundamentals: Focus on solving 2 coding problems optimally - graph algorithms, DP, string manipulation
  2. AI/ML Basics: Learn fundamental ML concepts and algorithms - now tested in MCQs
  3. Product Thinking: Understand how technical decisions impact users and products
  4. System Design: Practice designing scalable systems with AI/ML components
  5. Practice Previous Year Papers: Solve Google OA papers from 2020-2025 to understand patterns
  6. Time Management: Practice completing 2 coding problems in 60 minutes, 18-20 MCQs in 30 minutes
  7. LeetCode Practice: Solve 200+ LeetCode problems focusing on Google tag (medium-hard difficulty)
  8. Googleyness Preparation: Prepare STAR stories demonstrating collaboration, innovation, and product impact
  9. Behavioral Prep: Prepare extended examples for behavioral round focusing on cultural fit
  10. Mock Tests: Take timed practice tests to improve speed and accuracy

Google 2025 Paper 1

Latest 2025 online assessment with coding problems and MCQs

Download PDF →

Google 2025 Paper 2

Additional 2025 questions with detailed solutions

Download PDF →

2025 Coding Examples

Collection of 2025 coding problems with solutions

View Examples →


Practice 2025 papers to stay updated! Focus on AI/ML basics and product thinking for best results.