Python generators simplify memory-efficient iteration using the yield keyword. This quiz helps you master Python’s generator functions, expressions, lazy evaluation, and internal working with practical examples.
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Which keyword is used to define a generator function?
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Question 3 of 15
3. Question
What is the output of this code?
def gen(): yield 1 yield 2
g = gen() print(next(g))
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Question 4 of 15
4. Question
What will happen if you keep calling next() on an exhausted generator?
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Question 5 of 15
5. Question
What type does a generator return?
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Question 6 of 15
6. Question
Which of the following is not true about generators?
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Question 7 of 15
7. Question
What is the use of the yield keyword?
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Question 8 of 15
8. Question
Which of the following is a valid use case for generators?
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Question 9 of 15
9. Question
What is the output of this code?
def count(): n = 1 while True: yield n n += 1
c = count() for _ in range(3): print(next(c))
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Question 10 of 15
10. Question
Can a generator function contain the return statement?
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Question 11 of 15
11. Question
Which method is automatically called when using next() on a generator?
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Question 12 of 15
12. Question
What will this code output?
def squares(): for i in range(3): yield i * i
print(list(squares()))
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Question 13 of 15
13. Question
What is a generator expression?
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Question 14 of 15
14. Question
What is the output of this generator expression?
gen = (x*2 for x in range(3)) print(next(gen))
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Question 15 of 15
15. Question
Which of the following is more memory-efficient for large sequences?
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Summary
Python generators enable powerful lazy evaluation for large or infinite data streams using the yield keyword. This quiz equips you with the practical and theoretical understanding of how generators work, where to use them, and how to write them efficiently.
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