Concurrent and Parallel Programming in Python Practice Exam
Concurrent and Parallel Programming in Python Practice Exam
About Concurrent and Parallel Programming in Python Exam
The Concurrent and Parallel Programming in Python exam is designed to assess a candidate's expertise in writing efficient Python programs that leverage concurrency and parallelism to optimize performance. It evaluates the ability to manage multitasking, handle asynchronous operations, and improve computational efficiency, particularly in scenarios involving high-volume data processing or real-time applications. This exam ensures that participants have a thorough understanding of Python's concurrency and parallel programming constructs and can effectively utilize them in practical, real-world projects.
Skills Required
- Differentiate between concurrency and parallelism, and identify scenarios where each approach is beneficial.
- Recognize the performance and resource implications of concurrent and parallel programming.
- Work with Python's threading module to create and manage threads.
- Understand thread safety, synchronization, and handling race conditions using locks and semaphores.
- Implement asynchronous programming using asyncio and coroutines in Python.
- Effectively manage event loops and asynchronous tasks for I/O-bound operations.
- Use Python’s multiprocessing module to execute CPU-intensive tasks in parallel.
- Handle inter-process communication using pipes and queues, and manage process pools.
- Understand the impact of Python's GIL on multi-threaded programs and strategies to overcome its limitations.
- Utilize libraries like concurrent.futures and frameworks such as Dask or Ray for advanced parallel computing.
- Debug and profile concurrent and parallel programs to identify bottlenecks and ensure efficiency.
- Optimize performance while ensuring thread/process safety and avoiding deadlocks.
Who should take the Exam?
- Individuals with foundational Python programming skills who want to deepen their expertise in concurrency and parallelism to handle complex programming tasks.
- Data Scientists and Engineers
- Software Engineers
- DevOps Professionals
- Students and Learners
- Developers familiar with concurrent programming in other languages who want to adapt their skills to Python.
Course Outline
The Concurrent and Parallel Programming in Python Exam covers the following topics -
Domain 1 - Threading
- Overview of Threading, Multiprocessing, and Asynchronous Programming
- Implementing Threading in Python
- Developing a Threading Class
- Building a Wikipedia Data Reader
- Creating a Yahoo Finance Data Reader
- Utilizing Queues and Designing a Master Scheduler
- Developing and Integrating a Postgres Worker
- Introduction to YAML Files
- Implementing a YAML File Reader
- Enhancing the Wiki Worker Functionality
- Optimizing All Workers and Adding a Monitoring Mechanism
- Finalizing and Cleaning Up the Program
- Understanding and Implementing Locking Mechanisms
Domain 2 - Multiprocessing
- Introduction to Python’s Multiprocessing Module
- Implementing Multiprocessing Queues
- Using Multiprocessing Pools for Task Management
- Mapping Multiple Arguments Using Multiprocessing Pools
- Handling Multiple Dynamic Arguments in Multiprocessing
- Checking List Elements Within Specified Ranges Using Multiprocessing
Domain 3 - Asynchronous Programming
- Introduction to Writing Asynchronous Programs in Python
- Managing Asynchronous Tasks
- Utilizing the Async Gather Method for Efficient Execution
- Implementing Async Timeouts
- Creating Asynchronous Loops for Repeated Operations
- Leveraging Asynchronous Libraries for Enhanced Functionality
- Understanding and Using the Async Wait Statement
- Combining Asynchronous Programming with Multiprocessing Techniques
