M2P Fintech Implementation Engineer Interview Questions and Answers

It was about DS Algo and Java garbage collection,multithreading

Q1. Low level design of rate limiter
Ans. 
Rate limiter is a system that controls the rate of traffic sent or received by a network interface.
• Implement a token bucket algorithm to track and limit the rate of requests

• Use a sliding window algorithm to track the number of requests within a specific time frame

• Consider using a distributed rate limiter for scalability and fault tolerance
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Q2. Springboot basics, java basics, future interfaces, basic sql queries
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Asked to do coding for RDBMS using any programing language

Q1. Machine coding: Design in-memory document search
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Q1. HLD: Design a ad-click aggregation service
Ans. 
Design an ad-click aggregation service for tracking and analyzing ad clicks.
• Use a distributed system to handle high volume of ad click data

• Implement real-time processing for immediate insights on ad performance

• Utilize a database to store aggregated ad click data for reporting and analysis
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Q1. Design a in-memory cache
Ans. 
Design a high-performance in-memory cache system for storing and retrieving data efficiently.
• Use a hash table or a key-value store to store data in memory

• Implement caching strategies like LRU (Least Recently Used) or LFU (Least Frequently Used) to manage cache eviction

• Consider using a distributed cache for scalability and fault tolerance

• Optimize cache size and expiration policies based on the application's data access
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Q1. Rating Service Low level design
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Q1. In memory sql db
Ans. 
In-memory SQL databases store data in memory for faster access and processing.
• In-memory databases keep all data in RAM for quicker retrieval compared to disk-based databases.

• Examples include SQLite in-memory database and Redis.

• They are commonly used for caching, real-time analytics, and temporary data storage.
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Q1. Machine coding round for SQL
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