Rounds 1–2 are fully visible · Round 3+ unlocked with a free account
GreedyInterval SchedulingBFSSimulation
Two medium-hard problems with retail context, plus the same MCQ section as SDE-1 but with harder SQL and DBMS questions. At SDE-2 level the OA filters more aggressively — expect one problem requiring heap or interval scheduling and one with explicit real-world constraints like truck capacity or inventory thresholds.
What to expect
- ›MCQ section: harder DBMS normalization, SQL window functions, and OS concurrency questions compared to SDE-1 — don't skip this
- ›Problem 1 (Medium): e.g., 'Schedule restocking deliveries to N stores from a warehouse given truck capacity and time windows — maximize stores served' — greedy interval scheduling with capacity constraint
- ›Problem 2 (Medium-Hard): e.g., 'Given a stream of purchase events, find the minimum inventory buffer needed at each store to prevent stockouts within a 3-day rolling window' — sliding window with running sum
- ›Must pass at least 80% of test cases — partial credit applies but a 0 on either problem usually screens you out
Practice on KnowEase.AI
K-way MergeHeapsHashMapTwo Pointers
Two medium problems back-to-back. Walmart SDE-2 interviewers explicitly want to see real-world constraints — memory limits, data volume, partial failures — folded into your complexity analysis. Solving the problem correctly but ignoring scale is treated as an incomplete answer.
What to expect
- ›e.g., 'Given product listings from N different third-party sellers, merge them into a single sorted catalog without duplicates' — k-way merge using a min-heap on (price, seller_id)
- ›e.g., 'Find the most frequently co-purchased product pairs from 10M transaction logs' — hash map pair counting; follow-up: streaming version with fixed memory budget
- ›Follow-up on problem 2: 'What if transactions are arriving in real-time at 50K/sec — how does your approach change?' — expects sketch algorithms or partitioned counting, not just 'I'd use Kafka'
- ›O(n²) solutions with a handwave about 'optimizing later' will not pass at SDE-2 — expected to arrive at better-than-quadratic before writing code
Practice on KnowEase.AI
Articulation PointsMSTUnion-FindAdvanced Graph Algorithms
One hard graph problem. Walmart's supply chain has 150+ distribution centers globally — problems directly reflect that topology. At this round the interviewer expects working code, not a high-level algorithm sketch. Edge cases (disconnected graph, negative weights, zero-inventory nodes) are tested explicitly.
What to expect
- ›e.g., 'Find all critical distribution hubs in the supply network whose removal disconnects any store from its nearest warehouse' — Tarjan's articulation point algorithm
- ›e.g., 'Find the minimum-cost set of new distribution routes to ensure every store has at least 2-hop redundancy' — MST variant with Kruskal and Union-Find
- ›Follow-up: 'What if 20% of the edges have uncertain availability modeled as probabilities — how do you make the routing decision?' — expects probabilistic path selection or risk-adjusted Dijkstra
- ›Writing pseudocode instead of runnable code when asked to code is a consistent rejection signal reported by candidates in 2024
Window FunctionsSelf-JoinsIndexingSchema DesignPartitioning
The round LeetCode-only preppers lose. Walmart Bengaluru loops have reported two hard SQL questions in this slot. The focus is window functions, inventory analytics, and schema design for retail operations — not just SELECT + JOIN. Expect at least one performance question about a query scanning billions of rows in a fact table.
What to expect
- ›Query: 'For each store region, rank the top 3 products by revenue in the last 30 days — include ties' — DENSE_RANK() OVER (PARTITION BY region ORDER BY SUM(revenue) DESC) with a CTE
- ›Query: 'Find all products that have been continuously out of stock for more than 15 days in any store this month' — LAG() window function to detect consecutive zero-inventory days
- ›Schema design: 'Design tables for a fulfillment center tracking inventory levels, pick operations, and restock events — how do you model the daily sell-through rate efficiently?'
- ›Performance: 'This query scans a 2B-row sales fact table daily — what partitioning strategy and index design would you apply?' — expects range partitioning on sale_date, B-tree on (store_id, product_id)
- ›Skipping the schema design component or only preparing window function syntax without retail context is a documented failure mode
Distributed InventorySearch ArchitectureCachingHigh Write Throughput
Design a retail-scale distributed system. Walmart's context: 10,500+ stores, 2M employees, 100M+ weekly US customers, and a Black Friday peak that generates ~1M product views per second. Interviewers probe consistency vs. latency tradeoffs hard — inventory correctness is non-negotiable even at the cost of latency. Systems must also work when regional infrastructure degrades.
What to expect
- ›e.g., 'Design Walmart's real-time inventory management system' — the core challenge is preventing overselling across concurrent POS terminal updates and online orders; expected answer covers distributed locks, optimistic concurrency with version vectors, and event-sourced inventory log
- ›e.g., 'Design Walmart.com product search' — store-aware availability filtering is the trap: a product shown as in-stock must reflect actual shelf inventory updated from POS, not just warehouse data
- ›Black Friday framing: 'Your inventory service handles 1M/s reads and 50K/s writes on Black Friday — walk me through your cache invalidation strategy when a product sells out'
- ›Offline-first requirement: 'A store's network goes down for 2 hours — what does the POS system do, and how do you reconcile when connectivity returns?'
- ›Designing a pure cloud-first system with no offline-store consideration is the most common senior-candidate failure mode in this round
Stakeholder ManagementImpact MeasurementTechnical Leadership
Behavioral and team fit at SDE-2 level. Walmart's org is large and matrixed — supply chain, store ops, e-commerce, and payments teams all intersect. They want engineers who can operate in that complexity without losing technical clarity. STAR format answers anchored with metrics are the baseline expectation.
What to expect
- ›Tell me about the most impactful project you shipped — what was the business metric it moved and how did you measure it? (Vague 'improved performance by 30%' without context on the baseline is a known rejection signal)
- ›Describe a time you had to align multiple stakeholders with conflicting priorities — what was the actual conflict and how did you resolve it technically, not politically?
- ›How do you handle accumulating technical debt while continuing to ship features against a hard product deadline?
- ›Generic 'I love Walmart because it's a big company' answers without connecting to specific retail engineering problems (inventory at scale, omnichannel fulfillment, peak load) consistently fall flat
