Download free Ola placement papers 2025 with coding questions and solutions. Access DSA problems, system design, interview process, eligibility, salary, and complete preparation guide for 2025-2026 campus recruitment.
Ola (ANI Technologies Pvt. Ltd.) is India’s leading mobility platform and one of the world’s largest ride-hailing companies. Founded in 2010 by Bhavish Aggarwal and Ankit Bhati, Ola is known for its technology-driven transportation, electric mobility, AI-powered services including Kruti multilingual AI agent, and digital innovation, serving millions of users across India and international markets.
Headquarters: Bengaluru, India
Employees: 7,000+ globally
Industry: Mobility, Technology
Revenue: $1.1+ Billion USD (2023)
Academic Requirements
Minimum CGPA Required for Placement in Ola:
10th Standard: 65% or 6.5 CGPA
12th Standard: 65% or 6.5 CGPA
Graduation: 65% or 6.5 CGPA (aggregate)
Degree: B.E./B.Tech/M.E./M.Tech/MCA in Computer Science, IT, ECE, EE, or related fields
Year of Study: Final year students and recent graduates (within 1 year)
Backlogs: No active backlogs at the time of selection
Branch Eligibility
Eligible Branches: CS, IT, ECE, EE, and related engineering streams
Programming Focus: Strong skills in Data Structures, Algorithms, and System Design
Experience: Freshers and up to 2 years experience (for entry-level SDE roles)
Additional Criteria
Coding Skills: Proficiency in at least one language (Java, C++, Python, Go)
Gap Years: Maximum 1 year gap allowed
Course Type: Full-time degrees only
Nationality: Indian citizens (for India roles)
SDE-1 Role
Primary Role: Software Development Engineer - 1 (Entry-level)
Salary Package: ₹16-22 LPA for freshers
Selection: Through Online Assessment (OA) and Technical Interviews
Test Validity: OA scores valid for current hiring cycle
Is 6.5 CGPA Good for Ola?
Yes, 6.5 CGPA (65%) is the minimum requirement for Ola. However, candidates with higher CGPA (7.5+ or 75%+) have better chances of selection. Strong coding skills, DSA knowledge, and system design understanding are equally important and can compensate for lower CGPA.
Ola CGPA Cutoff
Minimum: 6.5 CGPA (65%)
Average Selected: 7.5-8.0 CGPA (75-80%)
For Premium Roles: 8.0+ CGPA (80%+) preferred
Download free Ola placement papers 2025 with previous year questions, detailed solutions, exam pattern, and complete preparation guide. Access Ola last 5 years placement papers with solutions PDF download and practice with solved questions covering all sections.
2024 Placement Papers PDF
Download Ola placement papers 2024 PDF with previous year OA questions, solutions, and exam pattern analysis.
2025 Placement Papers PDF
Download latest Ola placement papers 2025 PDF with current year OA questions, solutions, and updated exam patterns.
2026 Preparation Guide
Prepare for Ola placement 2026 with expected OA pattern, question types, and comprehensive preparation strategy.
Get comprehensive access to Ola last 5 years placement papers with solutions PDF download covering 2020-2025 OA exams. These papers include:
Online Assessment Guide
Complete guide to Ola online assessment (OA) format, DSA problems, coding questions, and preparation strategy.
Campus Recruitment
College Visits - Through placement cells at top engineering colleges
Direct registration via college coordinators
Off-Campus Drives
Ola Careers Portal - Apply online for Software Engineer and other roles
Participate in Ola Tech Hunt, hackathons, and coding contests
Online Assessment
Direct Apply - olacabs.com/careers
Register with academic details and resume
The Ola Online Assessment (OA) is the first screening round that evaluates candidates’ coding skills, problem-solving abilities, and technical knowledge.
Online Coding Assessment (OA) - 90-120 minutes
Total Questions: 2-3 coding problems Total Marks: Variable (based on test cases passed) Negative Marking: No negative marking Platform: HackerRank or Ola’s internal tool
Section-wise Breakdown:
| Section | Questions | Difficulty | Time (Minutes) | Focus Area |
|---|---|---|---|---|
| Coding Problems | 2-3 | Medium-Hard | 60-90 | DSA, Algorithms |
| Debugging | 1-2 | Medium | 15-30 | Code Analysis |
Section Details:
Passing Criteria:
Success Rate: ~10-15% of candidates advance to technical interviews
Time Management Strategy:
Technical Interviews (2-3 rounds, 45-60 min each)
Format: Virtual (Zoom/Teams) or Onsite
Managerial/Team Fit Interview (1 round, 45 min)
Format: Senior engineer or manager
HR/Offer Discussion (20-30 min)
Format: HR Manager
| Phase | Duration | Key Activities |
|---|---|---|
| Online Assessment | 1 day | Coding, debugging |
| Technical Interviews | 1-2 weeks | DSA, system design |
| Managerial/Team Fit | 2-3 days | Ola values, teamwork |
| HR Discussion | Same day | Offer, negotiation |
| Result Declaration | 2-3 days | Offer letter, background check |
Ola, as a leading transportation company, has integrated agentic AI systems into its platform to improve user experience, optimize operations, and enhance customer service. The most notable example is Kruti, Ola’s multilingual AI agent designed to perform real-world tasks like booking taxis, ordering food, and providing customer support.
Kruti AI Agent
Multilingual AI Assistant: Ola’s flagship AI agent supporting multiple Indian languages
Capabilities:
Technology: Advanced NLP, speech recognition, and conversational AI
Ride Matching Algorithms
Intelligent Driver-Rider Matching: AI-powered algorithms for optimal ride allocation
Features:
Customer Support Automation
Automated Support Systems: AI agents handling customer queries and issues
Capabilities:
Predictive Analytics
Demand Forecasting: AI agents predicting ride demand patterns
Applications:
When evaluating the transportation company Ola on agentic-test, consider the following key metrics and frameworks:
Task Completion Rate: Measure the percentage of tasks successfully completed by Ola’s AI agents (e.g., successful ride bookings through Kruti).
Response Accuracy: Evaluate the correctness of AI agent responses to user queries and commands.
Error Rate: Track the frequency of failures, misunderstandings, or incorrect actions by agentic systems.
Uptime and Availability: Monitor system availability and downtime to ensure 24/7 service reliability.
Data Privacy: Assess how Ola’s agentic systems handle user data, location information, and payment details.
Authentication and Authorization: Evaluate security measures for AI agent interactions, especially for financial transactions.
Bias Detection: Test for potential biases in AI decision-making, particularly in driver allocation, pricing, or customer service.
Adversarial Testing: Conduct tests to ensure agents can handle malicious inputs or edge cases safely.
Response Time: Measure latency in AI agent responses (e.g., time to confirm booking, answer queries).
Natural Language Understanding: Evaluate how well agents understand user intent, especially in regional languages.
Conversation Quality: Assess the naturalness and helpfulness of interactions with AI agents.
User Satisfaction Scores: Collect feedback on user experience with Ola’s agentic systems.
Load Handling: Test agentic systems under high traffic conditions (peak hours, festivals, events).
Concurrent User Support: Measure how many simultaneous interactions agents can handle effectively.
Resource Efficiency: Evaluate computational resources required for agent operations.
Scalability Testing: Assess system performance as user base and transaction volume grow.
Conversion Rate: Measure how agentic systems impact booking completion rates.
Cost Reduction: Evaluate savings from automated customer support vs. human agents.
Revenue Impact: Assess contribution of AI agents to overall business revenue.
Customer Retention: Measure how agentic systems affect customer loyalty and repeat usage.
Scenario-Based Testing
Real-World Scenarios: Test agents with actual use cases
A/B Testing
Comparative Analysis: Compare agent performance with human agents
Continuous Monitoring
Real-Time Evaluation: Monitor agent performance in production
Define Clear Test Objectives
Establish specific goals for testing Ola’s agentic systems:
Create Comprehensive Test Scenarios
Develop test cases covering:
Implement Automated Testing
Use automated frameworks to:
Collect Real User Data
Gather production data to:
Establish Feedback Loops
Create mechanisms for:
Based on industry analysis and user reports, Ola’s agentic systems demonstrate:
Ola continues to invest in enhancing its agentic capabilities:
If you’re preparing for Ola interviews, you may encounter questions about agentic systems:
Answer: Design considerations include:
Answer: Testing approach:
Answer: Key metrics:
Key Takeaway: When evaluating the transportation company Ola on agentic-test, focus on reliability, security, user experience, and business impact. Ola’s investment in AI agents like Kruti demonstrates its commitment to leveraging autonomous systems for improved transportation services.
Array Manipulation
def max_subarray_sum(arr): max_sum = float('-inf') current_sum = 0 for num in arr: current_sum += num if current_sum > max_sum: max_sum = current_sum if current_sum < 0: current_sum = 0 return max_sumGraph Traversal
This problem is a classic example of finding the shortest path in a graph using algorithms like Dijkstra’s or Breadth-First Search (BFS). The problem statement typically involves a graph represented by an adjacency matrix or list, and two nodes (source and destination). The goal is to find the minimum number of edges or the minimum sum of weights from the source to the destination.
Example:
// Assuming a graph is represented using an adjacency matrix// int graph[V][V] = { {0, 4, 0, 0, 0, 0, 0, 8, 0},// {4, 0, 8, 0, 0, 0, 0, 11, 0},// {0, 8, 0, 7, 0, 4, 0, 0, 2},// {0, 0, 7, 0, 9, 14, 0, 0, 0},// {0, 0, 0, 9, 0, 10, 0, 0, 0},// {0, 0, 4, 14, 10, 0, 2, 0, 0},// {0, 0, 0, 0, 0, 2, 0, 1, 6},// {8, 11, 0, 0, 0, 0, 1, 0, 7},// {0, 0, 2, 0, 0, 0, 6, 7, 0} };// int src = 0;// int dest = 4;// int V = 9;// int dist[V];// bool sptSet[V];// for (int i = 0; i < V; i++)// dist[i] = INT_MAX, sptSet[i] = false;// dist[src] = 0;// for (int count = 0; count < V - 1; count++) {// int u = minDistance(dist, sptSet, V);// sptSet[u] = true;// for (int v = 0; v < V; v++)// if (!sptSet[v] && graph[u][v] && dist[u] != INT_MAX// && dist[u] + graph[u][v] < dist[v])// dist[v] = dist[u] + graph[u][v];// }// cout << "Shortest distance from " << src << " to " << dest << " is " << dist[dest];Dynamic Programming
This problem is a classic example of the Coin Change Problem, which is a variation of the Knapsack Problem. The goal is to find the minimum number of coins needed to make a given amount using a set of coin denominations. This problem can be solved using Dynamic Programming (DP) with a bottom-up approach.
Example:
int coinChange(int coins[], int m, int amount) { int dp[amount + 1]; dp[0] = 0; for (int i = 1; i <= amount; i++) dp[i] = INT_MAX; for (int i = 1; i <= amount; i++) { for (int j = 0; j < m; j++) { if (coins[j] <= i) { int sub_res = dp[i - coins[j]]; if (sub_res != INT_MAX && sub_res + 1 < dp[i]) dp[i] = sub_res + 1; } } } return dp[amount];}Design a Ride Matching System
Design a Payment Gateway
Customer Focus
Ownership
DSA Fundamentals
Arrays & Strings: Manipulation, searching, sorting
Trees & Graphs: Traversals, shortest path, DFS/BFS
Dynamic Programming: Memoization, tabulation
Hashing: Hash maps, sets
System Design Basics
Mobility Architecture: Ride matching, payments, driver allocation
Database Design: SQL vs NoSQL, normalization
API Design: RESTful APIs, authentication
Scalability: Load balancing, caching, sharding
“Tell me about yourself”
“Describe a time you worked in a team”
“Describe a challenging project you worked on”
“Why Ola?”
“Are you willing to relocate?”
“What do you know about Ola’s technology stack?”
Candidate Background: B.Tech Computer Science, 8.2 CGPA, 2 internships in backend development, strong DSA skills
Experience: The OA was conducted on HackerRank with 3 coding problems:
Time Allocated: 120 minutes
Approach: I solved all 3 problems correctly, focusing on optimal time complexity. The debugging section had 2 questions where I had to identify and fix bugs in provided code.
Result: Cleared OA, advanced to technical interviews
Questions Asked:
Approach:
Result: Positive feedback, advanced to next round
Questions Asked:
Approach:
Result: Strong performance, advanced to managerial round
Questions Asked:
Approach:
Result: Cultural fit confirmed, advanced to HR round
Questions Asked:
Result: Selected - Received offer for SDE-1 role with ₹18 LPA package
Tips:
Candidate Background: M.Tech Computer Science, 8.5 CGPA, 1 year experience in a product company, expertise in distributed systems
Experience: OA had 2 medium-hard coding problems:
Solved both problems optimally. Debugging section was straightforward.
Result: Cleared OA
Questions Asked:
Approach:
Result: Advanced to next round
Questions Asked:
Approach:
Result: Advanced to managerial round
Questions Asked:
Result: Advanced to HR round
Questions Asked:
Result: Selected - Received offer for SDE-2 role with ₹26 LPA package
Tips:
Candidate Background: B.Tech IT, 7.8 CGPA, Final year student, strong communication skills
Questions Asked:
Approach:
Result: Selected - Received offer for SDE-1 role
Tips:
DSA Mastery
Priority: Critical
Time Allocation: 50%
System Design & OOP
Priority: High
Time Allocation: 20%
Ola Values
Priority: High
Time Allocation: 20%
Aptitude & Communication
Priority: Medium
Time Allocation: 10%
| Level | Experience | Base Salary | Total Package | Typical Background |
|---|---|---|---|---|
| SDE-1 | New Grad | ₹10-16 LPA | ₹16-22 LPA | Fresh graduates, top colleges |
| SDE-2 | 2-4 years | ₹18-24 LPA | ₹24-30 LPA | 2-4 years experience |
| Senior SDE | 5-8 years | ₹28-40 LPA | ₹40-55 LPA | Senior developers |
| Lead Engineer | 8+ years | ₹55+ LPA | ₹70 LPA+ | Architects, tech leads |
| Role | Level | Total Package | Requirements |
|---|---|---|---|
| QA Engineer | Entry-Mid | ₹7-12 LPA | Testing, automation |
| Product Manager | Mid-Senior | ₹18-35 LPA | Product sense, tech background |
| Data Scientist | Mid | ₹12-25 LPA | ML, analytics |
| DevOps Engineer | Entry-Mid | ₹8-18 LPA | Cloud, automation |
Hiring Trends 2025
Increased Virtual Hiring: More online assessments and interviews
System Design Emphasis: More focus in interviews
Diversity Hiring: Special drives for women and underrepresented groups
Process Changes
Online Assessments: More debugging and scenario-based questions
Team Fit Round: Mandatory for all SDE hires
Faster Offers: Reduced time from interview to offer
New Initiatives
Ola Tech Hunt: Coding competition for hiring
Student Programs: Internships, Ola Tech Internship
Internal Referrals: Employee referral program
Company Growth
Product Expansion: More engineering and data roles in India
Product Innovation: Mobility, electric vehicles, fintech
Global Mobility: Opportunities to work abroad
Ready to start your Ola preparation? Focus on DSA, system design, and Ola company values. Practice mock interviews and build strong STAR stories.
Pro Tip: Consistent practice on LeetCode and HackerRank is key. Understand Ola’s values and be ready to demonstrate them in behavioral rounds.
Ola placement papers are previous year question papers from Ola recruitment tests and interview rounds. These papers help students understand the exam pattern and prepare effectively.
Yes, all Ola placement papers on our website are completely free to access and download. You can practice unlimited questions without any registration or payment.
We provide Ola placement papers from recent years including 2024 and 2025. Our collection is regularly updated with the latest questions and exam patterns.
Ola placement process typically includes online assessment, technical interview, and HR interview rounds. See the placement process section for complete details.
Ola interview process typically consists of 2-3 rounds: online test, technical interview, and HR interview. Some roles may have additional rounds.
Ola eligibility criteria for freshers include minimum percentage requirements, degree requirements, and other criteria. Check the eligibility section for detailed information.
The minimum CGPA required varies by role. Check the eligibility criteria section for specific requirements.
Ola salary for freshers (2025-2026): SDE-1: ₹16-22 LPA (entry-level, fresh graduates from top colleges), SDE-2: ₹24-30 LPA (2-4 years experience), Senior SDE: ₹40-55 LPA (5-8 years), Lead Engineer: ₹70 LPA+ (8+ years). Other roles: QA Engineer (₹7-12 LPA), Product Manager (₹18-35 LPA), Data Scientist (₹12-25 LPA), DevOps Engineer (₹8-18 LPA). All figures are total annual compensation including base salary, performance bonuses, stock options (ESOPs), and benefits.
Ola SDE-1 (Software Development Engineer - 1) is the primary entry-level role for freshers: Role: Software Development Engineer - 1 (Entry-level position), Salary Package: ₹16-22 LPA for freshers, Selection: Through Online Assessment (OA) and Technical Interviews, Responsibilities: Backend/frontend development, system design, API development, working on ride matching, payment systems, or AI agents like Kruti, Growth Path: SDE-1 → SDE-2 → Senior SDE → Lead Engineer, Skills Required: Strong DSA, system design basics, proficiency in Java/C++/Python/Go.
Ola offers comprehensive benefits: Flexible Working (hybrid/remote options), Health Insurance (comprehensive coverage for employee and family), Stock Grants (ESOPs for engineers and above), Learning & Development (internal training, certifications, conference attendance), Work-Life Balance (employee assistance programs, wellness programs), Career Growth (fast-track promotions, global mobility opportunities), Free Rides (discounted or free Ola rides), Food & Snacks (free meals, snacks at office), Modern Office (state-of-the-art facilities in Bengaluru, Mumbai, Delhi).
To prepare for Ola placement: 1. Understand eligibility criteria (6.5+ CGPA, no backlogs), 2. Study exam pattern (OA with DSA problems, technical interviews, system design), 3. Practice previous year Ola placement papers with solutions, 4. Master DSA fundamentals (arrays, trees, graphs, DP) - solve 100+ problems on LeetCode/HackerRank, 5. Learn system design basics (ride matching, payment systems, scalability), 6. Prepare for technical interviews (coding, algorithms, projects), 7. Prepare for behavioral rounds (Ola values: customer focus, innovation, ownership), 8. Take mock tests to improve time management. See the preparation strategy section for detailed guidance.
Focus on: DSA Fundamentals (arrays, trees, graphs, dynamic programming, string manipulation) - 50% of preparation time, System Design (scalability, load balancing, caching, database design, API design) - 20% of preparation time, Coding Practice (solve 100+ problems on LeetCode, HackerRank, Codeforces) - critical, Ola Values & Behavioral (customer focus, innovation, ownership, STAR stories) - 20% of preparation time, Core CS Subjects (OOPs, DBMS, OS, Networking) - 10% of preparation time. See the preparation strategy section for detailed topic breakdown.
Practice at least 5-10 Ola placement papers from previous years (2020-2025). Focus on: Solving all coding problems with optimal solutions, Understanding question patterns and difficulty levels, Time management (solve problems within time limits), Reviewing solutions and learning different approaches, Practicing debugging questions. Additionally, solve 100+ problems on LeetCode/HackerRank covering arrays, trees, graphs, DP, and system design.
Ola placement is considered moderately difficult to difficult: Online Assessment: Medium-Hard difficulty, requires solving 2-3 DSA problems optimally, Technical Interviews: Challenging, focus on system design and scalability, Success Rate: ~10-15% of candidates advance from OA to interviews, Competition: High, especially for SDE-1 roles from top colleges. However, with proper preparation (strong DSA, system design, coding practice), it is achievable. Focus on solving 100+ coding problems and understanding system design principles.
Reasons to join Ola: Innovation & Technology (work on cutting-edge AI agents like Kruti, ride matching algorithms, payment systems), Scale & Impact (serve millions of users, solve real-world transportation problems), Growth Opportunities (fast-track career growth, work on diverse projects), Learning & Development (exposure to distributed systems, AI/ML, scalability challenges), Competitive Compensation (₹16-22 LPA for freshers, ESOPs, comprehensive benefits), Modern Tech Stack (work with latest technologies, microservices, cloud infrastructure), Electric Mobility (contribute to sustainable transportation solutions), Startup Culture (fast-paced, ownership, innovation).
Ola primarily hires from CS, IT, ECE, and EE branches for technical roles. However: Eligible Branches: CS, IT, ECE, EE, and related engineering streams, Strong DSA and system design skills are essential regardless of branch, Some roles (Product Manager, Data Scientist) may accept candidates from other backgrounds with relevant skills, Focus on demonstrating strong coding and problem-solving abilities.
Ola has offices in multiple Indian cities: Primary Locations: Bengaluru (headquarters), Mumbai, Delhi, Hyderabad, Other Locations: Chennai, Pune, Kolkata, Relocation: May be required depending on team and role, Hybrid/Remote: Ola offers flexible working options (hybrid/remote) for many roles, Discuss relocation preferences during HR interview. Most engineering roles are based in Bengaluru.
Ola: Indian company, strong in AI agents (Kruti), electric mobility focus, ₹16-22 LPA for SDE-1, good growth opportunities, strong in ride-hailing and food delivery. Uber: Global company, strong brand, ₹18-25 LPA for SDE-1, excellent learning opportunities, global mobility options. Zomato: Food delivery focus, ₹15-20 LPA for SDE-1, strong in logistics and delivery optimization, good work culture. Recommendation: Choose based on your interests - Ola for AI/mobility tech, Uber for global exposure, Zomato for food tech. All offer competitive packages and good learning opportunities.
Ola (Product-Based): Higher salary (₹16-22 LPA vs ₹3-6 LPA), better learning (work on real products, system design), faster growth, ownership and impact, challenging work. Service-Based (TCS, Infosys, Wipro): Lower salary (₹3-6 LPA), slower growth, less ownership, more stable, training programs. Recommendation: If you want higher salary, faster growth, and product experience, Ola is better. If you prefer stability and training, service-based companies may suit you. However, Ola offers better career prospects for ambitious engineers.
Last updated: November 2025