Akamai Coding Interview Questions
25 Akamai 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 Akamai interviewer values, and a FAQ section.
Showing 12 problems of 25
- #3mediumfrequently asked
3. Longest Substring Without Repeating Characters
Find the length of the longest substring with all unique characters. Akamai connects this directly to sliding-window analysis of edge-server request logs — the same technique detects the longest burst of distinct client IPs in a time window without repeated connections.
- #15mediumfrequently asked
15. 3Sum
Find all unique triplets in an array that sum to zero. Akamai asks 3Sum to assess whether candidates can layer sorting on top of a two-pointer scan and handle duplicate pruning cleanly — the same deduplication discipline required in log-aggregation pipelines at scale.
- #49mediumoccasionally asked
49. Group Anagrams
Group strings that are anagrams of each other. Akamai uses this to probe hash key design — choosing the right canonical form (sorted string vs. character frequency vector) is the same trade-off engineers face when designing cache keys for edge routing rules.
- #56mediumfrequently asked
56. Merge Intervals
Merge all overlapping intervals and return the non-overlapping result. Akamai uses this to model time-range coalescing in traffic analytics — merging overlapping maintenance windows or caching TTL intervals across thousands of edge nodes is a direct application of this algorithm.
- #139mediumoccasionally asked
139. Word Break
Determine if a string can be segmented into words from a dictionary. Akamai frames this as rule-matching in edge logic — determining whether a URL path can be decomposed into a sequence of known routing tokens is the same dynamic programming problem applied to real-time request handling.
- #146mediumfrequently asked
146. LRU Cache
Design a cache that evicts the least-recently-used entry when full. Akamai is one of the largest CDN operators in the world — LRU cache design is not an abstract exercise here, it is a direct description of the eviction policy running on thousands of edge servers handling billions of requests per day.
- #200mediumfrequently asked
200. Number of Islands
Count connected components of land cells in a 2D grid. Akamai uses this to probe graph traversal skills — the same BFS/DFS component labeling appears in network topology analysis, where connected PoP clusters in an infrastructure map must be identified and counted.
- #207mediumfrequently asked
207. Course Schedule
Determine if all courses can be completed given prerequisite dependencies. Akamai maps this to dependency resolution in edge configuration deployments — detecting a circular dependency (cycle in the DAG) is the difference between a successful config rollout and a cascading outage.
- #215mediumfrequently asked
215. Kth Largest Element in an Array
Find the kth largest element without fully sorting the array. Akamai asks this to test knowledge of Quickselect and heap-based selection — the same algorithms used for real-time percentile computation on edge server latency metrics without the cost of a full sort.
- #238mediumfrequently asked
238. Product of Array Except Self
Compute for each element the product of all other elements, without using division. Akamai asks this to test prefix/suffix scan thinking — the no-division constraint forces an elegant two-pass pattern that mirrors checksum computation across distributed edge nodes.
- #322mediumfrequently asked
322. Coin Change
Find the minimum number of coins to make a given amount. Akamai uses this to probe classic unbounded knapsack DP — the same recurrence structure appears in packet fragmentation optimization where the goal is to minimize the number of segments needed to transmit a payload of a given size.
- #347mediumfrequently asked
347. Top K Frequent Elements
Return the k most frequent elements in an array. Akamai frames this as a real edge-analytics problem: finding the top K most requested URLs from billions of daily log events. The heap vs. bucket-sort trade-off maps directly to what's feasible in streaming vs. batch pipelines.