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Sure, here's a technical phone screen round interview question for SDE II role at Microsoft: How would you implement a cache replacement algorithm for a web server serving millions of requests per day considering factors like cache size, hit rate, and latency? Please provide an efficient algorithm and explain its working principles.

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Indented and Formatted Code:```To implement a cache replacement algorithm for a web server serving millions of requests per day, I would follow the following steps:1. Choose a suitable cache replacement algorithm: - LRU (Least Recently Used) algorithm is widely used in industry because of its simplicity and efficiency. - LFU (Least Frequently Used) and FIFO (First In, First Out) are other commonly used algorithms.2. Determine an appropriate cache size: - Depending on the size of web content, forecasted number of requests, and available memory resources, an optimal cache size can be calculated. 3. Measure the hit rate of the cache: - To determine whether a cached item is frequently accessed, the hit rate of the cache should be measured. - If the hit rate is consistently high, then the cached item should be kept in the cache. - If the hit rate is low, then it should be removed from the cache to free up space for other items.4. Consider the latency of the algorithm: - The latency of the algorithm represents the amount of time taken to retrieve an item from the cache. - To reduce the latency, a suitable algorithm should be chosen that allows for the speedy retrieval of frequently accessed data.Based on these factors, an efficient algorithm can be created. Here is pseudo-code for a cache replacement algorithm that makes use of LRU technique:CacheAlgorithm(cache_size, hit_rate_threshold) Initialize a doubly linked list to represent cache Initialize a hash table that maps cache key to a corresponding node in the doubly linked list Iterate over the web content to be cached: If the item exists in the cache: Update its position in the doubly linked list Increment its hit count Else: If the cache is full: Remove the least recently used item from the cache by removing the tail node from the doubly linked list Remove the key from the hash table Add the new item to the front of the doubly linked list Add the key and corresponding node to the hash table If cache size exceeds the threshold: Check the hit rate of all the items in the doubly linked list Remove the item with the lowest hit rate to optimize cache sizeHyperlinks for reference:- More about cache replacement algorithms: https://en.wikipedia.org/wiki/Cache_replacement_policies- Understanding the principles of LRU: https://www.geeksforgeeks.org/lru-cache-implementation/ ```

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