Confluent Coding Interview Questions
25 Confluent coding interview problems with full optimal solutions — 8 easy, 12 medium, 5 hard. Every problem ships with multiple approaches (brute-force first, then the optimal), complexity tables for each, company-specific tips on what an Confluent interviewer values, and a FAQ section.
Showing 12 problems of 25
- #9mediumfoundational
9. LRU Cache
Design an O(1) get/put cache with least-recently-used eviction — Confluent uses it because the same doubly-linked-list + map idea powers consumer-side caches sitting in front of partition reads.
- #10mediumfoundational
10. Number of Islands
Count connected components of '1' cells in a grid — Confluent uses it because the BFS/DFS flood is the same shape as discovering live partitions across a Kafka cluster.
- #11mediumfoundational
11. Course Schedule
Decide if all courses can be finished given prerequisite edges — Confluent uses it because cycle detection in a DAG maps directly onto detecting circular dependencies in Kafka Connect pipelines.
- #12mediumfoundational
12. Kth Largest Element in an Array
Find the kth-largest value in an unsorted array — Confluent uses it to probe heap intuition, which lines up with top-K streaming aggregates over a Kafka topic.
- #13mediumfoundational
13. Product of Array Except Self
Return the array of products of all elements except self without using division — Confluent uses it to test prefix/suffix passes, the same pattern used for partition-aware running aggregates.
- #14mediumfoundational
14. Find the Duplicate Number
Find the repeated number in an n+1 array of values [1..n] using O(1) space — Confluent uses it because cycle-detection on an implicit graph mirrors offset duplication checks in a Kafka log.
- #15mediumfoundational
15. Longest Increasing Subsequence
Return the length of the longest strictly increasing subsequence — Confluent uses it to probe DP/binary-search hybrid thinking, which connects to ordered-offset analysis over a Kafka log.
- #16mediumfoundational
16. Coin Change
Find the fewest coins summing to a target — Confluent uses it as the canonical unbounded knapsack to check that you reason about state transitions before extending to streaming DP over a Kafka topic.
- #17mediumfoundational
17. Top K Frequent Elements
Return the k most frequent elements — Confluent uses it because top-K aggregation is one of the bread-and-butter ksqlDB queries running over a Kafka topic.
- #18mediumfoundational
18. Insert Delete GetRandom O(1)
Design a set with O(1) insert, remove, and random selection — Confluent uses it because the dense-array + map trick is the same one that lets a consumer group pick a partition uniformly during sampling.
- #19mediumfoundational
19. Subarray Sum Equals K
Count contiguous subarrays summing to k — Confluent uses it because the prefix-sum hash trick is the same shape as counting offset windows that hit a target inside a Kafka log.
- #20mediumfoundational
20. Top K Frequent Words
Return the k most frequent words ordered by frequency then lexicographically — Confluent uses it because word-frequency aggregations are the canonical KStream demo over a Kafka topic.