Python’s multiprocessing module enables true parallel execution by spawning separate processes for tasks. It helps developers overcome the Global Interpreter Lock (GIL) and efficiently handle CPU-bound operations across multiple cores
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What is the main purpose of Python’s multiprocessing module?
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Question 2 of 15
2. Question
Which method is used to start a process?
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Question 3 of 15
3. Question
Which class is used to create a new process?
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Question 4 of 15
4. Question
Which function is used to wait for a process to complete?
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Question 5 of 15
5. Question
What must be included when writing multiprocessing code for Windows?
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Question 6 of 15
6. Question
Which Python feature limits the effectiveness of multithreading in CPU-bound tasks?
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Question 7 of 15
7. Question
Which method of multiprocessing.Pool runs a function on multiple inputs in parallel?
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Question 8 of 15
8. Question
Which object is used to safely share data between multiple processes?
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Question 9 of 15
9. Question
Which multiprocessing object allows the creation of shared lists and dictionaries?
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Question 10 of 15
10. Question
What is the purpose of daemon=True in Process?
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Question 11 of 15
11. Question
Which of these is best for CPU-intensive tasks?
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Question 12 of 15
12. Question
What will is_alive() method return for a completed process?
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Question 13 of 15
13. Question
Which method submits a single job to a Pool?
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Question 14 of 15
14. Question
What is the main advantage of multiprocessing.Pool over multiple Process objects?
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Question 15 of 15
15. Question
Which process creation method allows tracking multiple jobs with callbacks?
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Summary
The Python multiprocessing module allows parallel processing using multiple CPU cores, helping overcome GIL limitations for CPU-heavy tasks. This quiz covered Process, Pool, Queue, Manager, apply_async(), and best practices for safe and efficient parallel computing.
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