POC Concurrency & Multithreading 3

Question 1

What does setting thread.daemon = True do before starting a thread?

Accepted Answer

Makes the thread run in the background and automatically exit when the main program ends

Answer

Raises the thread's execution priority to the highest level

Answer

Allocates additional memory to the thread for large workloads

Answer

Prevents the thread from being interrupted by signals

Question 2

Which asyncio function is the standard entry point for running a top-level coroutine in Python 3.7+?

Accepted Answer

asyncio.run()

Answer

asyncio.start()

Answer

asyncio.execute()

Answer

asyncio.loop.run()

Question 3

What is a threading.Semaphore and how does it differ from a Lock?

Accepted Answer

A Semaphore maintains a counter allowing a configurable number of threads to access a resource concurrently, unlike a Lock which allows only one

Answer

A Semaphore is identical to a Lock but faster in execution

Answer

A Semaphore blocks all threads; a Lock only blocks the current thread

Answer

A Semaphore is used for process synchronization; a Lock is for thread synchronization

Question 4

Which module provides the Process class for spawning separate OS processes in Python?

Accepted Answer

multiprocessing

Answer

threading

Answer

asyncio

Answer

concurrent

Question 5

What is the purpose of threading.Event in Python multithreading?

Accepted Answer

To enable threads to signal each other — one thread sets the event, others wait for it

Answer

To schedule a thread to run at a specific time

Answer

To log thread execution events to a file automatically

Answer

To limit the maximum number of simultaneously active threads

Question 6

Which multiprocessing objects are used to share state (a single value or an array) safely between processes?

Accepted Answer

multiprocessing.Value and multiprocessing.Array

Answer

multiprocessing.Lock and multiprocessing.Semaphore

Answer

multiprocessing.Queue and multiprocessing.Pipe

Answer

multiprocessing.Event and multiprocessing.Barrier

Question 7

How does the GIL impact CPU-bound tasks in a multithreaded Python program?

Accepted Answer

It prevents threads from executing Python bytecode in parallel, so adding threads provides little or no speedup for CPU-bound work

Answer

It improves performance by letting Python optimize CPU usage across threads

Answer

It has no measurable impact because CPU-bound tasks rarely block

Answer

It transparently routes threads to separate CPU cores to maximize throughput