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.
- #1easyfrequently asked
1. Two Sum
Find two indices in an array whose values sum to a target. Akamai asks this as a warm-up to probe hash map reasoning and O(n) thinking — the kind of constant-time lookup that matters when processing billions of log entries per day at the edge.
- #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.
- #4hardoccasionally asked
4. Median of Two Sorted Arrays
Find the median of two sorted arrays in O(log(m+n)) time. Akamai asks this to test binary search mastery at its hardest — computing the p50 latency across two sorted measurement arrays without merging them maps directly to how distributed edge performance dashboards aggregate data from multiple regions.
- #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.
- #20easyfrequently asked
20. Valid Parentheses
Determine whether a string of brackets is correctly nested. Akamai asks this to test stack-based reasoning — the same pattern underpins parsing HTTP header fields and configuration syntax that edge servers process at high throughput.
- #21easyfrequently asked
21. Merge Two Sorted Lists
Merge two sorted linked lists into one sorted list. Akamai asks this because merge is a fundamental building block for external sort — the same pattern used to merge sorted access logs from thousands of edge servers into a single chronological stream.
- #23hardfrequently asked
23. Merge K Sorted Lists
Merge K sorted linked lists into one sorted list efficiently. Akamai considers this a flagship problem — merging sorted access logs from thousands of geographically distributed edge servers into a single time-ordered stream is a production use case that maps directly to this algorithm.
- #42hardfrequently asked
42. Trapping Rain Water
Calculate how much water can be trapped between elevation bars. Akamai uses this as a two-pointer mastery test — the same left-and-right boundary tracking logic appears in buffer capacity analysis for edge server memory pools where headroom must be calculated between high-water marks.
- #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.
- #53easyfrequently asked
53. Maximum Subarray
Find the contiguous subarray with the largest sum. Akamai ties this directly to network throughput analysis — Kadane's algorithm is the canonical one-pass scan for detecting the peak burst window in a stream of per-second byte-delta measurements.
- #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.
- #70easyoccasionally asked
70. Climbing Stairs
Count the distinct ways to climb n stairs taking 1 or 2 steps at a time. Akamai uses this as an entry point into dynamic programming — the recurrence relation is immediately applicable to retry-strategy combinatorics and caching policy analysis.
- #121easyfrequently asked
121. Best Time to Buy and Sell Stock
Find the maximum profit from one buy and one sell transaction in a price array. Akamai frames this as a running-minimum scan — the same streaming pattern used to compute latency baselines across billions of edge-server events in a single pass.
- #127hardoccasionally asked
127. Word Ladder
Find the shortest transformation sequence from one word to another changing one letter at a time. Akamai ties this to BFS in routing graphs — finding the minimum number of hops between two network states where each hop changes exactly one configuration parameter is the same shortest-path problem on an implicit graph.
- #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.
- #206easyfrequently asked
206. Reverse Linked List
Reverse a singly linked list in place. Akamai uses this to verify that candidates can reason about pointer manipulation without extra memory — a discipline that matters in high-throughput packet-processing code where heap allocations are expensive.
- #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.
- #239hardfrequently asked
239. Sliding Window Maximum
Return the maximum value in each sliding window of size k. Akamai uses this as a real-time analytics primitive — computing peak request rates, maximum connection counts, or highest latency values in a rolling time window across edge server telemetry is exactly this algorithm at production scale.
- #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.
- #704easyoccasionally asked
704. Binary Search
Search a sorted array for a target value in O(log n) time. Akamai uses binary search as a litmus for algorithmic correctness — the off-by-one bug in the loop condition is the same class of error that causes out-of-bound reads in high-performance C++ networking code.