Understanding Async IO in Python

What problem does async IO primarily address?

• Data corruption

• High CPU usage

• Communication lag

• Lack of memory

In asynchronous programming, how do tasks execute?

• Sequentially, one after another

• Simultaneously, using multiple threads

• Only after the previous task is completed

• In overlapping periods of time

What is a key characteristic of a coroutine?

• It cannot be paused

• It runs until completion without interruption

• It can maintain its state between pauses

• It is a subset of a larger program

What is the main difference between concurrency and parallelism?

• Concurrency is only used in Python

• Concurrency uses multiple threads, while parallelism uses a single thread

• Concurrency involves overlapping task execution, while parallelism involves simultaneous execution

• Concurrency is slower than parallelism

What keyword is used to define a coroutine in Python?

• await

• async

• def

• yield

What function in the async IO module allows grouping of coroutines for concurrent execution?

• execute

• run

• gather

• create_task

Why is it necessary to use the 'await' keyword in a coroutine?

• To pause execution and yield control

• To define a coroutine

• To start a coroutine

• To end a coroutine

What is the purpose of the 'create_task' function in async IO?

• To terminate a coroutine

• To create a new thread

• To execute a coroutine immediately

• To create a task from a coroutine

How does the async IO module help computer chips?

• By cooling them down

• By enabling them to take turns executing tasks

• By reducing their power consumption

• By increasing their speed

What is the result of running the modified breakfast code concurrently?

• It does not complete

• It takes 10 seconds to complete

• It takes 3 seconds to complete

• It takes 5 seconds to complete