Practice Using The Multiprocessing Module (3.1) - Chapter 7: Concurrency and Parallelism in Python

Practice: Using the multiprocessing Module

Lesson 3.1Concurrency and Parallelism in Python

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11 questions

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4 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
5 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What module would you use for running CPU-bound tasks in Python?
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Think about parallel execution of processes.
Question 2.
What does GIL stand for?
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Is it related to threading or multiprocessing?
3Question 3.
What does the multiprocessing module help achieve?
- True parallelism
- Thread safety
- Memory sharing
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Focus on the nature of CPU-bound tasks.
4Question 4.
True or False: The GIL allows multiple threads in Python to run bytecode simultaneously.
- True
- False
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Remember the limitation posed by GIL.
Question 5.
Implement a multiprocessing solution for computing the factorial of numbers from 1 to 10, using the multiprocessing module to parallelize the computation.
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Recall how to define functions and invoke processes.
Question 6.
Discuss the limitations of using multiprocessing in a high-throughput application where latency is a concern. Suggest alternative approaches.
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Think about trade-offs related to performance and application needs.

7 more questions in this section. Enroll free to practise them all.