Rakuten
10+ GeeksMetrics Interview Questions and Answers
Q1. How to design large scale machine learning system
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
Q2. Architecture design for large scale app
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
Q3. Multithreading and multiprocessing use cases
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
Q4. Live coding to parse logs from a given file
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
Q5. System design for big data application
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
Q6. Hash Table implementation
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
Q7. Memory management in python
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.
Q8. how does random forest work
Q9. Merge the two sorted array
Q10. what is iverfitting
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