Ixl Software Engineer Interview Questions

This post was last edited by Anonymous on 2025-09-25 21:15. After reading the posts on the forum, it seems my situation is different from everyone else's. I initially applied for positions in NC, and

LeetCode #295: Find Median from Data Stream. Difficulty: Hard. Topics: Two Pointers, Design, Sorting, Heap (Priority Queue), Data Stream. Asked at IXL in the last 6 months.

LeetCode #598: Range Addition II. Difficulty: Easy. Topics: Array, Math. Asked at IXL in the last 6 months.

LeetCode #227: Basic Calculator II. Difficulty: Medium. Topics: Math, String, Stack. Asked at IXL in the last 6 months.

## Problem Parse an HTML string to extract structure, validate tag nesting, or transform content. ## Likely LeetCode equivalent No close equivalent. ## Tags coding, strings, parsing, stack, phone-screen

## Problem Process log entries to extract, deduplicate, or aggregate unique IDs from a log stream. ## Likely LeetCode equivalent No close equivalent. ## Tags coding, arrays, hash-table, phone-screen

## Problem Find the level in a binary tree with maximum coverage or sum, using BFS level-order traversal. ## Likely LeetCode equivalent Related to LC 662 Maximum Width of Binary Tree. ## Tags coding, binary-tree, BFS, phone-screen

## Problem Randomly place items in a grid or array according to given constraints, ensuring uniform distribution. ## Likely LeetCode equivalent No close equivalent. ## Tags coding, math, randomization, phone-screen

## Round 1 - System Design ## Problem Design a scoring server that receives raw feature vectors in real time and returns model scores within 20ms p99. The model is a gradient boosted tree (100 MB serialized). The server handles 50K requests/sec at peak. ## Requirements - Latency: p99 < 20ms end-to-end (network + inference). - Throughput: 50K RPS peak, 10K RPS average. - The model is updated daily; zero-downtime rollout required. - Feature input: JSON payload, ~50 float fields. ## Design Points ``` Load Balancer -> Scoring Fleet (stateless workers) Workers: deserialize JSON -> validate -> run model -> return score Model loaded in-process (no subprocess call) Blue/Green deploy: new model warmed up, traffic shifted atomically ``` ## Discussion Questions - How do you manage model warm-up time when spinning up new instances? - How do you validate incoming features for schema drift before scoring? - What metrics do you instrument: latency histogram, score distribution, error rate? ## Follow-ups 1. How do you A/B test two model versions in production with consistent user assignment? 2. What happens when a feature is missing in the payload — impute, reject, or score with default? 3. How do you handle a latency spike caused by a single slow feature transformation? 4. How would the design differ for a deep learning model that requires a GPU?

## Problem Maintain a real-time leaderboard of student submissions, supporting efficient top-K queries and score updates. ## Likely LeetCode equivalent Related to LC 1244 Design A Leaderboard. ## Tags coding, heap, design, phone-screen

## Problem Process text input through a series of transformation rules such as tokenization, substitution, or formatting. ## Likely LeetCode equivalent No close equivalent. ## Tags coding, strings, parsing, phone-screen