10+ GeeksMetrics Interview Questions and Answers

Designing large scale ML systems requires careful consideration of data, infrastructure, algorithms, and deployment.

• Identify the problem and define the objectives

• Collect and preprocess large amounts of data

• Choose appropriate algorithms and train models

• Deploy models on scalable infrastructure

• Monitor and evaluate performance regularly

• Continuously improve the system based on feedback

• Consider ethical and legal implications

• Collaborate with cross-functional teams

• Examples: Google's ...read more

Architecture design for large scale app involves distributed systems, microservices, and scalability.

• Use distributed systems to handle large amounts of data and traffic

• Implement microservices to break down the app into smaller, manageable components

• Ensure scalability by designing for horizontal scaling and load balancing

• Use caching and database sharding to improve performance

• Implement fault tolerance and disaster recovery measures

• Consider using containerization and orchestrat...read more

Multithreading and multiprocessing are used to improve performance by executing multiple tasks simultaneously.

• Multithreading is useful for I/O-bound tasks, such as web scraping or file processing.

• Multiprocessing is useful for CPU-bound tasks, such as image processing or scientific computing.

• Both can be used together to achieve maximum performance.

• Care must be taken to avoid race conditions and deadlocks.

• Python's Global Interpreter Lock (GIL) limits the effectiveness of multit...read more

Live coding to parse logs from a given file

• Choose a programming language

• Read the file line by line

• Use regular expressions to extract relevant information

• Store the extracted data in a structured format

System design for big data application involves selecting appropriate technologies, designing data architecture, and ensuring scalability.

• Choose appropriate technologies for data storage, processing, and analysis

• Design data architecture that can handle large volumes of data

• Ensure scalability by using distributed systems and load balancing

• Consider security and privacy concerns

• Examples of technologies: Hadoop, Spark, NoSQL databases, distributed file systems

Hash table is a data structure that maps keys to values using a hash function.

• Hash function maps keys to indices in an array

• Collisions can occur when two keys map to the same index

• Collision resolution techniques include chaining and open addressing

• Hash tables have O(1) average case time complexity for insert, delete, and search operations

Python uses automatic memory management through garbage collection.

• Python uses reference counting to keep track of objects in memory.

• When an object's reference count reaches zero, it is deleted by the garbage collector.

• Python also uses a cyclic garbage collector to handle circular references.

• Memory can be managed manually using the 'gc' module.

• Python's memory management is efficient and transparent to the programmer.