Which of the following is NOT a benefit of using generators?
A. Improved memory efficiency
B. Faster execution than loops
C. Easier debugging
D. Lazy evaluation
Answer: Option C
Solution(By Examveda Team)
Option A: Improved memory efficiency.This option is a well-known benefit of using generators. Generators produce values lazily, meaning they only generate values when requested, rather than storing all values in memory at once. This lazy evaluation mechanism results in improved memory efficiency, especially when dealing with large datasets or infinite sequences.
Option B: Faster execution than loops.
This option is not correct because generators do not inherently execute faster than loops. In fact, due to the additional functionality they provide, such as suspending and resuming execution with each yield statement, generators might introduce a slight overhead. While generators offer other benefits like improved memory efficiency and lazy evaluation, faster execution compared to loops is not one of them.
Option C: Easier debugging.
This option is incorrect. Debugging generator functions can be more challenging compared to debugging regular functions or loops. The flow of execution in generator functions involves suspending and resuming with the yield statement, which can make it harder to track. Additionally, debugging generator functions might require specialized techniques or tools to inspect the state of the generator object and the values yielded during iteration.
Option D: Lazy evaluation.
This option is actually a benefit of using generators. Generators support lazy evaluation, meaning they produce values on-demand as they are requested, rather than eagerly generating all values upfront. Lazy evaluation allows for efficient memory usage and can be particularly useful when working with large datasets or infinite sequences.
So, the correct answer is indeed Option C: Easier debugging, as it does not align with the typical benefits associated with using generators.
What is a generator in Python?
A. A function that generates random numbers
B. A special type of list
C. A way to define classes
D. A function that yields values one at a time
How is a generator different from a regular function?
A. A generator uses the return keyword
B. A generator can yield multiple values
C. A generator uses the break statement
D. A generator uses the continue statement
What is an advantage of using generators for large datasets?
A. They use more memory
B. They are slower than loops
C. They are easier to implement
D. They use less memory
How do you define a generator function in Python?
A. Using the generator keyword
B. Using the def keyword and yield statement
C. Using the gen keyword
D. Using the function keyword
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