Pinterest Software Engineer Interview Questions

WaterBuffalo - "One of the most challenging situations was joining a new startup to realize that they did not figure out their core product and that customers were dissatisfied and leaving in droves. I worked together with the leaders to understand the core issue and the reasons that the customers were leaving. Then together with the team, we set out to map out those core issues and the gaps that we had in our product. We then worked towards designing a new solution to address those gaps and build an MVP. W"See full answer

Ashish G. - "Rate Limiter is to limit the number of request from a particular IP Address. Rate limiter will block the IP address to reduce the load on server. It should be highly available and handle concurrent requests. Blocked IP addresses should be kept in a pool which is present in shared cache. We need to keep threshold value after it reaches threshold value it should start blocking IP address. All these ip address to be kept in No SQL DB. Batch will run that will clear the cache and delete all the bloc"See full answer

Aymen D. - "It would have been more interesting to focus on the system design rather than the Trie DS, Interviewee could have just mentioned the Trie and passed to things more important. Interviewee should have focused on the factors on which he wants to scale the API servers, popularity of the query parts ? region may be ? A hash of many factors ? Caches should have definitely be discussed, Cache eviction policies, Cache invalidation managements... Interviewee should have mentioned which kind of API pro"See full answer

Dinar M. - "Even more faster and vectorized version, using np.linalg.norm - to avoid loop and np.argpartition to select lowest k. We dont need to sort whole array - we need to be sure that first k elements are lower than the rest. import numpy as np def knn(Xtrain, ytrain, X_new, k): distances = np.linalg.norm(Xtrain - Xnew, axis=1) k_indices = np.argpartition(distances, k)[:k] # O(N) selection instead of O(N log N) sort return int(np.sum(ytrain[kindices]) > k / 2.0) `"See full answer