Interview experience
Ola interview experiences covering Online Assessment, Technical Round 1, Technical Round 2, Hiring Manager / HR. Round-wise notes, common questions, and what candidates report clears each stage.
Ola Interview Questions and Answers (2026)
Ola, formally ANI Technologies, is an Indian mobility platform founded in 2010 and known for ride-hailing and large-scale marketplace logistics. Its engineering teams build real-time matching, mapping, and payments systems used by millions of riders and drivers. Ola recruits software engineers from campuses for backend, mobile, and data roles.
Explore: Interview experience · HR interview questions · Overview · Placement papers · Preparation guide
Overview
Ola’s fresher SDE loop is a 4-5 stage process where system-design questions are consistently mapped onto its actual mobility-platform product - ride matching, real-time location tracking, surge pricing, payments.
HR interview questions
Ola HR interview questions with sample answer frameworks - motivation, relocation, STAR stories, and culture-fit prompts.
Ola interview process at a glance
| Round | Duration | What they test |
|---|---|---|
| Online Assessment | 90-120 min | 2-3 coding problems (DSA) |
| Technical Round 1 | 45-60 min | DSA + detailed project discussion |
| Technical Round 2 | 45-60 min | DSA / system design basics |
| Hiring Manager / HR | 30-45 min | Behavioural + team fit |
Ola online assessment: quick answers
These answers are compiled from public candidate reports and prep guides, not from an official Ola notice, and drives differ. Some public campus reports come from Ola Electric drives, which use a similar coding test. The instruction screen of your own test is the final word.
| Question | Answer |
|---|---|
| How many questions and how long? | Usually 2-3 coding problems in 90-120 minutes. A 2022 campus report describes 3 mandatory easy-to-medium problems in 90 minutes; a February 2024 SDE-1 report describes a single medium coding question. Some drives add a debugging section. |
| Does it have negative marking? | No public report mentions any. It is a coding test scored on test cases. |
| Is there a sectional cutoff? | Not reported. Where a debugging section appears, candidates describe one combined shortlist. |
| How much is the cutoff? | Not published; shortlists are relative to the batch. Candidate-reported eligibility is 6.5+ CGPA or 65% throughout, from CS, IT, ECE or EE related branches, with no active backlogs. |
| Which platform is it on? | HackerRank, in most reports. |
| What topics come up? | Arrays, strings and DP: a knapsack variant where items can be reused, a ticket-counter queue simulation, 0/1/2 sorting, and Kadane-style subarray problems. |
| Is there system design for freshers? | Often, yes. 2024-2025 SDE-1 reports include one system design interview after the OA. |
Ola interview questions by role
These topics come from Glassdoor, Code360, GfG and LeetCode Discuss interview reports. They show the level and style, not a fixed list.
| Role | Reported questions and topics |
|---|---|
| SDE-1 (campus) | 90-minute coding OA; a 40-50 minute technical round with a special stack supporting popMin and popMax in O(1); SQL and DBMS questions; a project deep dive (one candidate was rejected for not explaining their project). |
| SDE-1 (off-campus, 2024-2025) | Three interviews including system design, then HR with salary and team matching (February 2024); DSA, core Java, system design and HR, covering an LRU cache, designing a hashmap, multithreading and a custom load balancer (October 2025). |
| SDE-2 | A one-day loop: a 60-minute DP problem, a 90-minute LLD and HLD round on Ola’s internal notification system, and a 45-minute hiring-manager round (July 2024 offer). URL shortener and ride-matching HLD also appear. |
Asked across every role: a 5-10 minute introduction, a resume and project walk-through, strengths and weaknesses, and why Ola.
Online Assessment
A 90-120 minute HackerRank test with 2-3 DSA problems plus a debugging section where you identify and fix bugs in provided code snippets. Campus drives in 2024-2025 also report easy-medium mixes: string/array questions, DP variants (e.g. unbounded knapsack twists), and queue simulations such as a ticket counter where each person re-joins the queue after one ticket until their quota is met.
Common questions
- Maximum-sum subarray (Kadane’s algorithm variant)
- Lowest common ancestor in a binary tree
- Shortest path in a weighted graph (Dijkstra’s algorithm)
- Merge k sorted arrays or lists (heap / divide-and-conquer)
- Dutch-national-flag style sorts (0/1/2) with stability follow-ups in some drives
- Debugging exercises on provided code snippets
Technical Round 1
Opens with project discussion, then a coding problem - often with a follow-up asking you to optimize for a different constraint (e.g. space instead of time).
Common questions
- Design a data structure supporting insert, delete, and getRandom in O(1)
- Optimize your solution’s space complexity, and justify the trade-off
- Design a ride-matching system for Ola (scalability, load balancing, real-time matching)
- Detailed walkthrough of your resume project
Technical Round 2
Pairs more DSA with system-design questions scoped directly to Ola’s mobility-platform domain. Experienced-candidate reports also include URL shortener HLD (schema, cache eviction, expiry cleanup, click analytics) and, in one documented campus loop, a 90+ minute distributed-database design with configurable consistency vs availability modes - harder than a standard fresher HLD, but worth knowing exists on some tracks.
Common questions
- Implement an LRU cache (HashMap + doubly linked list) or a rate limiter (sliding / token bucket)
- Design Ola’s real-time location-tracking system (WebSockets, Redis caching, Kafka events)
- Design a surge-pricing system - real-time demand calculation, pricing algorithm
- Design a payment gateway with transaction safety (ACID properties, idempotency, retries)
- Geospatial check: is a rider inside a service polygon to show nearby cabs? (point-in-polygon, geohash, Redis GEO)
- Special stack with popMin / popMax in O(1) average (auxiliary min/max stacks)
Round-by-round narratives are on the Ola interview experience page.
Managerial / Team Fit Interview
Checks alignment with Ola’s values - ownership, customer focus - often through a specific incident-response scenario rather than abstract questions.
Common questions
- Describe a time you demonstrated ownership
- How would you handle a production issue at 2 AM?
- Describe a conflict you resolved in a team
- Architecture decisions and trade-offs from your technical rounds, revisited
HR / Offer Discussion
A closing conversation on background, motivation, and logistics.
Common questions
- Why Ola, and what do you know about its business?
- Are you willing to relocate to Bengaluru?
- What are your salary expectations?
- Joining timeline
Sample answer frameworks for each of these are on the Ola HR interview questions page.
Ola (ANI Technologies) vs Ola Electric vs Krutrim
Three separate companies share the Ola name. Ola, the ride-hailing app, is run by ANI Technologies. Ola Electric is a separately listed electric-scooter and battery maker. Krutrim is the group’s AI company. Each hires on its own and issues its own offer letters.
The coding tests look alike: public Ola Electric campus reports describe the same 90-minute, 3-problem format. The interviews differ: ride-hailing Ola asks about ride matching, surge pricing and location tracking, while Ola Electric and Krutrim teams hire for their own vehicle and AI products, so expect their domain questions instead. This page covers ride-hailing Ola; check the company name on your invite before you prepare domain questions.
Common technical interview questions and answers
Q: Explain Kadane’s algorithm for maximum-sum subarray and its complexity.
Kadane’s algorithm makes a single pass, maintaining current_sum = max(num, current_sum + num) at each index and best = max(best, current_sum). The insight is that a prefix with a negative running sum can never help a later subarray, so you discard it and restart at the current element. Time is O(n) and space is O(1). If all elements are negative, initialise best to the first element rather than 0, otherwise you incorrectly return 0 for a non-empty array.
Q: How do you find the lowest common ancestor of two nodes in a binary tree?
For a general binary tree, recurse from the root: if the current node is null or equals either target, return it. Recurse left and right; if both sides return non-null, the current node is the LCA, otherwise return whichever side is non-null. This is O(n) time and O(h) space for the recursion stack, where h is the tree height. In a binary search tree you can do better - walk down from the root and the first node whose value lies between the two targets is the LCA, which is O(h) time and O(1) space iteratively.
Q: How does Dijkstra’s algorithm work, and when does it fail?
Dijkstra maintains a distance array initialised to infinity except the source at 0, and repeatedly extracts the unvisited node with the smallest known distance from a min-heap, relaxing each outgoing edge by checking whether dist[u] + weight(u,v) improves dist[v]. With a binary heap it runs in O((V + E) log V). It fails on graphs with negative edge weights, because once a node is finalised it is never revisited - use Bellman-Ford (O(VE)) there instead. For road-network routing like Ola’s ETA problem, A with a geographic heuristic is the practical refinement.
Q: Design a data structure supporting insert, delete, and getRandom in O(1).
Combine a dynamic array (list) with a hash map from value to that value’s index in the array. Insert appends to the array and records the index in the map. Delete looks up the index, swaps that element with the last element of the array, updates the swapped element’s index in the map, pops the array tail, and erases the key. getRandom picks a uniform random index into the array. Every operation is O(1) average, and the swap-with-last trick is what avoids the O(n) shift a naive array removal would cost.
Q: How do you implement an LRU cache, and why those specific data structures?
Use a hash map from key to node plus a doubly linked list ordered most-recently-used at the head and least-recently-used at the tail. On get, look up the node in O(1) and move it to the head. On put, insert at the head, and if size exceeds capacity, remove the tail node and delete its key from the map. The doubly linked list is required because removing an arbitrary node in O(1) needs both prev and next pointers; a singly linked list would force an O(n) scan to find the predecessor. Both operations are O(1) with O(capacity) space.
Q: How would you design a real-time driver-location tracking system?
Drivers push a location ping every few seconds over a persistent WebSocket (or MQTT) connection to a gateway layer, which publishes each ping to a Kafka topic partitioned by driver_id so ordering per driver is preserved. A consumer writes the latest position into Redis using a geospatial index (GEOADD/GEORADIUS) keyed by city or geohash cell, so a nearby-driver query is a sublinear radius lookup rather than a full table scan. Raw pings stream to cold storage such as S3 for analytics and trip replay rather than sitting in the hot path. The core trade-offs are ping frequency versus battery and bandwidth, and accepting eventual consistency on location because a driver position a second out of date is acceptable.
Q: How would you design a surge-pricing engine?
Divide the city into geohash or S2 cells and, per cell, compute a rolling window of open ride requests versus available drivers - typically over a 2-5 minute tumbling window from the same event stream that feeds dispatch. The surge multiplier is a function of that demand-supply ratio, clamped to a floor of 1.0x and a regulatory ceiling, and smoothed so it does not oscillate between consecutive windows. Cache the multiplier per cell in Redis with a short TTL so fare quotes read it in single-digit milliseconds, and lock the quoted multiplier to the booking for a fixed validity period so the rider is not charged a different price than they were shown. Log every multiplier decision for audit, since pricing is regulated.
Q: What does idempotency mean for a payment API, and how do you implement it?
An idempotent API returns the same result and causes the same single side effect no matter how many times the client retries the same request - essential because a network timeout leaves the client unsure whether the charge went through. Implement it by having the client send an Idempotency-Key header (a UUID) with the request; the server does an atomic insert of that key into a unique-indexed table inside the same database transaction as the charge. If the insert succeeds, process the payment and store the response against the key; if it violates the unique constraint, return the stored response instead of charging again. This relies on the ACID guarantees of the transaction - doing the check and the charge in two separate steps reintroduces the race.
Q: How would you design a ride-matching system for Ola?
Split the problem: ingest live driver locations (stream partitioned by city/geohash), maintain a hot index of available drivers per cell (Redis GEO or similar), and on each booking request run a matching worker that ranks candidates by ETA, acceptance rate, and fairness constraints (avoid starving distant drivers). Persist trip state in a transactional store; push assignment over mobile push/WebSocket. Discuss failure modes: driver goes offline after accept, double booking (optimistic locking on driver status), and surge multiplier locked at quote time. At fresher depth you do not need full ML dispatch - a greedy nearest-driver with caps is a valid starting point if you state assumptions.
Q: How do you check if a user is inside an area to show available cabs?
Precompute service polygons per city (or union of geohash cells). On each app open, read the rider’s GPS point and test membership: ray-casting for arbitrary polygons, or geohash prefix lookup if zones align to cells. Cache polygon metadata in CDN/edge config; do the point test on device or a low-latency edge function. If outside, short-circuit before hitting dispatch - saves load and sets correct UX (waitlist, different product). Mention GPS inaccuracy: buffer the polygon or require two consecutive fixes inside before showing inventory.
Frequently asked questions about Ola interviews
Does the Ola online assessment have negative marking?
No public report mentions negative marking. Ola’s online assessment is a coding test - campus reports describe 3 easy-to-medium problems in 90 minutes, all mandatory - scored on test cases, with a debugging section on some drives.
Is the Ola coding test hard?
Usually easy to medium. Campus reports list array, string and DP problems such as a knapsack variant where items can be picked more than once, and a February 2024 SDE-1 report describes a single medium online question. The interviews that follow are harder, with system design even for SDE-1 in 2024-2025 reports.
What is the Ola interview process for freshers?
Usually 4-5 stages, based on student reports: 1. Online Coding Assessment (90-120 min) - 2-3 DSA problems plus debugging questions on HackerRank. 2. Technical Interview 1 (45-60 min) - a coding problem plus detailed project discussion. 3. Technical Interview 2 (45-60 min) - more DSA plus system-design questions scoped to Ola’s domain (ride matching, real-time location, surge pricing, payments). 4. Managerial/Team Fit Interview (45 min) on Ola’s values (customer focus, ownership). 5. HR/Offer Discussion (20-30 min). Total timeline is roughly 2-4 weeks from application to offer.
What questions are asked in Ola interviews?
DSA problems reported include maximum-sum subarray, lowest common ancestor in a binary tree, Dijkstra’s shortest path, an O(1) insert/delete/getRandom data structure, and LRU cache implementation. System-design prompts are consistently scoped to Ola’s actual product - design a ride-matching system, a real-time location-tracking system (WebSockets, Redis, Kafka), a surge-pricing engine, or a payment gateway with transaction safety (ACID, idempotency, retries).
How many rounds are there in the Ola interview?
Most fresher SDE drives run 4-5 stages: an online assessment, two technical interviews (DSA/project, then DSA/system-design), a managerial/team-fit round, and a closing HR round. Some drives compress the managerial round into the final technical interview - confirm the exact structure from your placement cell.
What system-design questions does Ola ask?
Ola’s system-design prompts map directly onto its mobility-platform product: ride-matching algorithms, real-time driver-location tracking (candidates report using WebSockets/Redis/Kafka in their answers), surge-pricing calculation, and payment-gateway transaction safety. Expect to discuss scalability, fault tolerance, and consistency trade-offs (CAP theorem, database sharding) even at a fresher-appropriate depth.
How should I prepare for Ola interviews?
Practice medium-to-hard DSA (arrays, trees, graphs, LRU-cache-style problems) since both technical rounds lean on live coding. For system design, practice mapping generic patterns (rate limiters, distributed caches, real-time tracking) onto ride-hailing-shaped problems specifically - ride matching, surge pricing, location tracking. Prepare STAR-format stories for ownership, handling ambiguity, and a production-issue scenario, since Ola’s managerial round asks for these directly.
Is this page about ride-hailing Ola or Ola Electric?
This page covers ride-hailing/mobility-platform Ola (ANI Technologies), not Ola Electric, which is a separate, now-publicly-listed company focused on electric vehicles. The interview content here - ride matching, surge pricing, location tracking, Bengaluru-based roles - reflects the mobility-tech business.
What Ola interviewers score
Mobility-platform (ANI Technologies) loops typically filter on timed OA completeness, then live coding and marketplace reasoning. Panels score:
- Finished medium DSA on HackerRank-style OAs (graphs, heaps, windows) with working submits.
- Ride-marketplace intuition - matching, location updates, surge, caching - explained simply, not as a 40-slide deck.
- Ownership stories that fit India-scale ops: scrappy fixes, clear Result metrics, Bengaluru relocation honesty.
Soft fail modes: confusing Ola Electric with ride-hailing Ola, remote-only answers on Bengaluru SDE forms, or design talk without finishing the OA.
Does Ola ask system design for freshers?
Many fresher loops stay DSA-heavy after the OA, while experienced and some SDE-1 processes add HLD prompts such as URL shorteners or ride-matching sketches. Prepare a simple marketplace design you can draw in ten minutes even if your invite looks coding-only.
How to prepare for Ola interviews
- Loop map: Online Assessment; Technical Round 1; Technical Round 2; Hiring Manager / HR. Confirm timings from the drive mail.
- Technical depth: Drill the Q&A section until answers are spoken, not read. Know every resume project cold.
- Experiences: Study round-wise notes on the interview experience page - real prompts beat generic lists.
- HR: Three STAR stories + a specific “Why Ola?” on the HR page.
Who usually fits and who struggles
Good fit: You like marketplace problems, you will relocate to Bengaluru, and you can finish medium DSA under a clock. You can talk about matching drivers to riders without a 40-slide deck.
Harder path: You only grind aptitude; you freeze on graphs; you want remote-only on a Bengaluru SDE form; you confuse Ola Cabs with Ola Electric.
A practical OLA prep plan
Use this as a default four-week loop before a OLA drive. Adjust to your invite timeline.
- Week 1: Timed aptitude + one easy coding problem a day, plus a one-page cs fundamentals sheet. Open the Ola previous-year sets on this site and mark which sections ate your time. Write the timing on a sticky note. That number is more useful than a 200-problem LeetCode streak you never timed.
- Week 2: Most Ola drives fail people on the first timed paper or OA. Do three timed sits this week. After each one, log every miss in a two-column note: topic and why (slow, wrong formula, bad edge case, panic). The next sit should only add problems from that log.
- Week 3: Technical interviews at Ola usually want a clean project story, one or two CS fundamentals, and (if the process has it) a behavioral or values round. Write STAR stories on paper, not in your head. Practise “Why Ola?” with one concrete product, lab, or business line - not a slogan.
- Week 4: Two full mocks, then only error-log topics - no new patterns. If you still have energy, redo only the questions you failed in week 2.
10-day crash version: Days 1-4 timed DSA, days 5-7 resume projects + CS fundamentals, days 8-9 mock interviews out loud, day 10 HR STAR stories on the HR page.
Compensation notes
Candidate-reported fresher/early-career packages for OLA often cite ₹16-22 LPA SDE-1 (confirm letter). Treat forum numbers as ranges, not guarantees - confirm the written offer letter for your team and location.
Sources
Interview process details and sample questions on this site are editorially organized from public candidate reports and hiring write-ups, with round structure, prep notes, and company-specific framing added by our team. For round-by-round stories, see the OLA interview experience page. For OA papers and coding drills, use OLA on placementpapers.app. OA quick answers and role-wise questions draw on Glassdoor Ola interview reviews (2024-2025), Code360 and GfG Ola and Ola Electric campus interview experiences, and a LeetCode Discuss Ola SDE-2 offer post (July 2024).
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