Design & OOD

Data-structure design is a LinkedIn fingerprint. Across its tagged pool, the hardest and most-repeated prompts are not graph traversals or DP tables. They are "build this class." a Premium question is the second most-frequent problem LinkedIn asks, behind only a Premium question near the top, and both are design problems. Insert/Delete/GetRandom and the cache family sit right alongside them. Bucket LinkedIn's coding surface by topic and one label dwarfs the rest: Design. If you drill one shape before a LinkedIn loop, drill the class with O(1) operations.

These recur because they test something a one-function algorithm cannot. A design problem makes you pick the structures, define the API, hold an invariant true across a whole sequence of calls, and hit a tight time bound on every operation at once. That is closer to writing a real component than solving a puzzle, which is exactly the engineering LinkedIn weights heavily. It is also what the newer AI-assisted round probes hardest: confirmed prompts there are LRU, LFU with a ranking extension, and an online interval store where you choose the backing structure. Get fluent at design here and you have prepped two rounds at once.

The LinkedIn angle

The grade lives in the follow-up. The first version barely counts. LinkedIn's design rounds run in two acts. First you build a class that passes the operations. Then the interviewer extends it: make every operation O(1), now swap least-recently-used eviction for least-frequently-used, now add a rank query, now make popMax run in log time instead of linear. Reports from the AI round say it plainly, the follow-ups matter more than the initial solution. So build the base cleanly and leave seams. A working class you can extend beats a clever one you cannot.