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Python Programming - Generators

Exercise : Generators - General Questions
  • Generators - General Questions
1.
What is a generator?
A function that generates random numbers
An iterable object that allows lazy evaluation of values
A built-in Python module for creating HTML documents
A keyword used for dynamic memory allocation
Answer: Option
Explanation:
In Python, a generator is a type of iterable, similar to a list or tuple, but it allows lazy evaluation of values. It generates values on-the-fly and does not store them in memory all at once, making it memory-efficient.
2.
How is a generator function different from a regular function?
Generator functions use the keyword yield to produce a sequence of values
Regular functions use the keyword yield for lazy evaluation
Generator functions use the keyword return to produce a sequence of values
Regular functions use the keyword yield for immediate value generation
Answer: Option
Explanation:
Generator functions in Python use the yield keyword to produce a sequence of values. When a generator function is called, it returns a generator object that can be iterated over to get values one at a time.
3.
How can you create a generator object?
Using the new_generator() constructor
By calling a generator function with gen_func()
Using the Generator() class
By applying the yield keyword in any function
Answer: Option
Explanation:
A generator object is created by calling a generator function using its name followed by parentheses, such as gen_func().
4.
What happens when a generator function encounters the yield keyword?
The function terminates immediately
The value is returned, and the function's state is saved
The function raises an exception
The function restarts from the beginning
Answer: Option
Explanation:
When a generator function encounters the yield keyword, it returns the specified value and saves its state. The next time the generator is called, it resumes execution from where it left off.
5.
What is the primary advantage of using generators?
Generators consume less memory compared to lists
Generators can only produce infinite sequences
Generators allow for random access of elements
Generators are faster than regular functions
Answer: Option
Explanation:
Generators are memory-efficient as they produce values on-the-fly and do not store the entire sequence in memory. This makes them suitable for handling large datasets or infinite sequences without using excessive memory.
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