Makemytrip Placement Papers 2026

Meta Description: Master MakeMyTrip Placement Papers 2026 with exact exam pattern, aptitude step solutions, DSA & system design insights, and HR interview Q&A. Download now!

Introduction

MakeMyTrip (MMT) stands as India’s undisputed leader in the online travel and hospitality space, headquartered in Gurugram, Haryana. Since its inception, the platform has transformed how millions of Indians book flights, hotels, trains, buses, and holiday packages. With billions in annual Gross Merchandise Value (GMV) and a massive engineering footprint, MMT operates at the intersection of consumer tech, logistics, and real-time data processing. The company’s 2026 hiring cycle is aggressively targeting software engineers, data analysts, and backend developers to scale its infrastructure, enhance AI-driven recommendations, and handle hyper-scaling during festive travel seasons.

Candidates consistently apply for MakeMyTrip SDE and data roles because of the company’s engineering culture, which emphasizes ownership, product thinking, and real-world impact at scale. Engineers get to work with high-throughput booking systems, low-latency search engines, and machine learning pipelines that directly influence customer experiences. MMT’s work culture promotes agility, cross-functional collaboration, and continuous learning, backed by competitive compensation, comprehensive ESOPs, and flexible hybrid work arrangements from its Gurugram, Bengaluru, and remote hubs.

Clearing the MakeMyTrip selection process in 2026 requires more than textbook theoretical knowledge. Recruiters and engineering managers heavily evaluate Data Structures & Algorithms (DSA) fundamentals, system design intuition, and product sense. The interview stages test your ability to write production-grade code, handle concurrency and distributed systems, and articulate trade-offs under constraints. This comprehensive guide breaks down the exact placement paper structure, provides fully solved aptitude questions, and delivers targeted interview strategies to help you secure an offer.

Exam Pattern 2026

Section	Questions	Time	Difficulty
Quantitative & Logical Aptitude	20	30 mins	Moderate-High
Verbal & Reading Comprehension	15	20 mins	Moderate
Core CS & Technical MCQs	15	20 mins	High
DSA Coding Round (LeetCode Medium-Hard)	2	60 mins	High
System Design / Data Analysis Case	1	30 mins	High
Total	53	~3 hrs	Overall High

Quantitative Aptitude Questions

Question 1: A train crosses a 120m platform in 15 seconds and a 240m platform in 21 seconds. Find the length of the train. Solution: Let train length = L, speed = v. Equation 1: (L + 120)/15 = v Equation 2: (L + 240)/21 = v Set equal: (L+120)/15 = (L+240)/21 → 21L + 2520 = 15L + 3600 → 6L = 1080 → L = 180m.

Question 2: A merchant marks an article 40% above cost price and gives a 15% discount. Find the profit percentage. Solution: Let CP = 100. MP = 140. Discount = 15% of 140 = 21. SP = 140 - 21 = 119. Profit = 119 - 100 = 19. Profit% = 19%.

Question 3: P can complete a work in 12 days, Q in 18 days. They work together for 4 days, then Q leaves. How many more days for P to finish? Solution: Work/day: P=1/12, Q=1/18. Together= (3+2)/36 = 5/36. Work in 4 days = 20/36 = 5/9. Remaining = 4/9. P alone: (4/9) ÷ (1/12) = 48/9 = 5.33 days (5⅓ days).

Question 4: The average of 50 numbers is 25. If two numbers, 4 and 18, are removed and replaced by 28, find the new average. Solution: Sum = 50×25 = 1250. Remove 4+18=22, add 28. Net change = +6. New sum = 1256. Total numbers still 50. New avg = 1256/50 = 25.12.

Question 5: A bag has 6 red, 4 blue, 5 green balls. Two balls drawn randomly. Probability both are same color? Solution: Total ways = C(15,2) = 105. Same color combos: C(6,2)+C(4,2)+C(5,2) = 15+6+10 = 31. Probability = 31/105.

Question 6: If 12 men or 18 women can complete a job in 28 days, how many days will 8 men and 15 women take? Solution: 12M = 18W → 1M = 1.5W. Total work = 18W × 28 = 504 woman-days. 8M = 12W. So 8M+15W = 27W. Days = 504/27 ≈ 18.67 → 18⅔ days.

Question 7: A sum doubles in 8 years at simple interest. Find rate of interest. Solution: SI = P. P = (P×R×T)/100 → 1 = (R×8)/100 → R = 12.5%.

Question 8: In how many ways can the letters of “BOOKKEEPER” be arranged? Solution: Total=10 letters. Repeats: O(2), K(2), E(3). Ways = 10! / (2!×2!×3!) = 3,628,800 / (2×2×6) = 3,628,800/24 = 151,200.

Question 9: The ratio of speeds of two cars is 4:5. If the faster car takes 2 hrs less for 400 km, find speed of slower car. Solution: Let speeds = 4x, 5x. Time diff: 400/4x - 400/5x = 2 → (5-4)400/20x = 2 → 400/20x=2 → x=10. Slower speed = 40 km/hr.

Question 10: A shopkeeper uses 950gm weight for 1kg and sells at CP. Find actual gain percent. Solution: Gives 950g, charges for 1000g. If CP per 1000g = 1000, actual cost for 950g = 950. Selling price charged = 1000. Gain = 50. Gain% = (50/950)×100 ≈ 5.26%.

Question 11: If log₁₀(2) = 0.3010, log₁₀(3) = 0.4771, find log₁₀(12). Solution: log(12) = log(3×4) = log3 + log4 = 0.4771 + 2log2 = 0.4771 + 0.6020 = 1.0791.

Question 12: A 200L mixture has milk:water = 3:2. How much water added to make ratio 2:3? Solution: Milk = (3/5)×200 = 120L. Water = 80L. Let x water added. 120/(80+x) = 2/3 → 360 = 160 + 2x → 2x=200 → x=100L.

Verbal Ability Questions

Q1. Error Spotting: Neither the manager (A) / nor his team members (B) / was aware of (C) / the system outage. (D) Ans: (C) Change “was” to “were”. Rule: With neither…nor, verb agrees with nearer subject (“members” → plural).

Q2. Synonym: The candidate’s argument was spurious and easily dismissed. Ans: False/genuine opposite → Bogus, counterfeit. “Spurious” means fake/invalid.

Q3. Para Jumble: A. Eventually, caching reduced latency by 60%. B. Developers noticed high database load. C. They implemented Redis for session management. D. The traffic spike was traced to unoptimized queries. Ans: BDCA. Logical flow identifies problem → diagnosis → action → result.

Q4. Fill in the blanks: The API response was ______ delayed due to network congestion and high server ______. Ans: severely, load

Q5. Reading Comprehension Inference: “Unlike legacy booking engines that rely on batch processing, real-time inventory systems fetch availability on-demand, reducing overbooking but increasing infrastructure costs.” Q: What does the author imply? Ans: Real-time systems improve accuracy at the expense of higher compute/infrastructure expenditure.

Q6. Antonym: The engineering roadmap proved highly volatile after market shifts. Ans: Stable, consistent, predictable.

Q7. Active/Passive Conversion: “The product team is designing a new loyalty algorithm.” Ans: A new loyalty algorithm is being designed by the product team.

Technical / Domain Questions

Q1. What happens when you type https://www.makemytrip.com/hotels and press Enter? Ans: DNS resolution → TCP handshake → TLS negotiation → HTTP GET request → CDN/WAF intercept → Load balancer routes to API gateway → Microservice (search/inventory) queries DB/Cache → Returns JSON/HTML. Browser parses, renders, and executes frontend JS.

Q2. How does connection pooling optimize DB performance in high-traffic booking systems? Ans: Reuses established DB connections instead of creating new ones per request. Reduces handshake overhead, limits concurrent open connections, prevents resource exhaustion, and maintains predictable latency during peak loads.

Q3. Differentiate ACID and BASE in distributed databases. Ans: ACID (Used in relational/transactional systems) guarantees Atomicity, Consistency, Isolation, Durability. Suitable for payments/bookings. BASE (Used in NoSQL/distributed) stands for Basically Available, Soft state, Eventual consistency. Favors availability and partition tolerance over strict consistency, ideal for user profiles, search indexes, and review caching.

Q4. How would you design an idempotent booking API endpoint? Ans: Use a client-generated unique idempotency key (header: Idempotency-Key: <UUID>). Store key in Redis/DB with status. Before processing, check existence. If exists, return cached response. If not, execute transaction, store result with key, return 200. Handle retries safely.

Q5. Explain CAP theorem in the context of MMT’s hotel inventory service. Ans: During network partitions, system must choose between Consistency and Availability. For real-time room pricing/availability, consistency is prioritized to prevent double booking (CP system). For non-critical features like recommendations, eventual consistency (AP) is acceptable. Partition tolerance is non-negotiable.

Q6. What is a race condition and how do you prevent it in concurrent Java/Python services? Ans: Occurs when two threads/processes access shared data simultaneously leading to unpredictable results. Prevent using atomic operations, database row-level locking (SELECT ... FOR UPDATE), distributed locks (Redis Redlock), or optimistic concurrency control (versioning/timestamps).

Q7. Why use a message queue (Kafka/SQS) in travel tech? Ans: Decouples services, handles traffic spikes, ensures asynchronous processing. Use cases: booking confirmation emails, payment reconciliation, loyalty point updates, analytics event streaming, inventory sync partners. Guarantees durability, ordering, and retry mechanisms.

Q8. Differentiate horizontal vs vertical scaling with travel booking examples. Ans: Vertical scaling: upgrading a single server’s CPU/RAM (limited, causes downtime, single point of failure). Horizontal scaling: adding more stateless app servers behind load balancer (highly elastic, fault-tolerant, cloud-native). MMT horizontally scales microservices, caches, and databases to handle Diwali/New Year traffic surges.

Coding Questions (for tech companies)

Problem 1: Hotel Booking Overlap Counter Problem: Given an array of booking check-in and check-out times [start, end], find the maximum number of overlapping bookings at any point in time. Approach:

• Extract all time points: mark start as +1, end as -1.
• Sort all points chronologically.
• Iterate while maintaining a current_guests counter. Track maximum.
• Handle same-time check-out before check-in to avoid false overlaps (sort end times before start if equal).
• Time: O(N log N) due to sorting. Space: O(N).
• Edge Cases: Same day bookings, midnight boundaries, empty array.

Problem 2: Cheapest Flight Route with Max Stops Problem: Given cities, flight costs, and maximum allowed stops k, find minimum cost from source to destination. Return -1 if unreachable within k stops. Approach:

• Model as weighted directed graph.
• Use BFS with level tracking or modified Dijkstra with stop constraint.
• Maintain dist[city] = minimum cost. Use queue storing [cost, city, stops_used].
• Only push to queue if stops_used < k. Early exit if destination reached.
• For large graphs, Bellman-Ford relaxation k+1 times works efficiently.
• Time: O(E log V) or O(kE) depending on approach. Space: O(V+E).

Interview Process

MakeMyTrip’s 2026 interview cycle for SDE and technical roles follows a structured 4-5 stage funnel:

1. Online Assessment (Codility/Hackerrank): 90-120 minutes. Includes aptitude, verbal, CS fundamentals, and two DSA problems (medium difficulty). Auto-graded with visible test cases.
2. DSA & Problem Solving (Round 1): Virtual interview (45-60 mins). Focus on arrays, trees, graphs, DP, and string manipulation. Interviewer evaluates problem decomposition, edge-case handling, and clean code. Expect follow-up optimization questions.
3. Low-Level Design / System Design (Round 2): 45-60 mins. Candidates design components like “Hotel Search API”, “Payment Gateway Integration”, or “Recommendation Engine”. Focus on scalability, data modeling, caching strategies, load balancing, and trade-offs. Product metrics discussion (latency, throughput, error budget) is crucial.
4. Technical Manager / Round 3: Deep dive into past projects, architecture decisions, debugging approaches, and behavioral alignment. May include live debugging or pair programming.
5. HR & Culture Fit: Compensation discussion, relocation (Gurugram/Bengaluru), growth plans, values alignment, and notice period negotiation.

Interview Tips

1. Master DSA Patterns: Focus on sliding window, two pointers, BFS/DFS, graph traversals, and DP. Practice medium LeetCode problems under 20 mins.
2. Think Product-First: Before coding, ask clarifying questions. Define constraints, edge cases, and success metrics. MMT values business impact.
3. System Design Structure: Use RECAP (Requirements, Estimation, Capacity, APIs, Data Model, Components, Trade-offs). Always start with scale assumptions (DAU, QPS, latency SLA).
4. Conduct Mock Interviews: Use Pramp, Interviewing.io, or peer mock rounds. Record yourself to catch pacing and communication gaps.
5. Prepare Real Projects: Document your role, architecture choices, and measurable outcomes. Be ready to defend technical trade-offs.
6. Stay Calm Under Load Questions: MMT interviewers love peak-traffic scenarios. Discuss caching, async processing, rate limiting, and graceful degradation.
7. Write Clean, Readable Code: Use meaningful variable names, modular functions, and inline comments. Test with sample inputs before declaring done.

HR Questions

Q1. Tell me about yourself. Sample: “I’m a Computer Science graduate with 2 years of backend development experience, focusing on scalable microservices and data pipelines. I recently built a real-time booking validator that reduced latency by 40%. I’m passionate about consumer tech and applying distributed systems knowledge to travel platforms like MakeMyTrip.”

Q2. Why MakeMyTrip? Sample: “MMT operates at massive scale with complex, low-latency problems like inventory synchronization and dynamic pricing. The opportunity to work on systems impacting millions during peak travel seasons aligns perfectly with my backend optimization skills and desire to build customer-centric products.”

Q3. Describe a time you handled a production failure. Sample: “During a Diwali sale, our checkout service hit 90% CPU due to a missing index. I analyzed APM metrics, added composite indexes, implemented query-level