Generators Questions and Answers

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Q1
What is a generator in Python?
  • A A function that returns an iterable generator object
  • B A special type of list that stores data
  • C A Python module for mathematical operations
  • D A built-in Python data type
Answer: Option A
Explanation: Generators are functions that return an iterable generator object and use yield statements to produce a sequence of values lazily.
Q2
Which keyword is used to create a generator function?
  • A yield
  • B return
  • C generate
  • D generator
Answer: Option A
Explanation: The yield keyword is used in generator functions to return values one at a time, pausing execution between calls.
Q3
What is the main advantage of using generators over lists?
  • A Memory efficiency
  • B Faster execution for all operations
  • C Easier to debug
  • D Better error handling
Answer: Option A
Explanation: Generators are memory efficient because they generate values on-the-fly and don't store all values in memory at once.
Q4
How do you create a generator expression?
  • A Using parentheses ()
  • B Using square brackets []
  • C Using curly braces {}
  • D Using angle brackets <>
Answer: Option A
Explanation: Generator expressions use parentheses () instead of square brackets [] used for list comprehensions.
Q5
What happens when a generator function calls yield?
  • A Function pauses and returns value
  • B Function terminates immediately
  • C Function starts from beginning
  • D Function raises an exception
Answer: Option A
Explanation: When yield is called, the function state is saved and execution pauses, resuming from that point on the next call.
Q6
Which method is used to get the next value from a generator?
  • A next()
  • B get()
  • C yield()
  • D iterate()
Answer: Option A
Explanation: The next() function is used to retrieve the next value from a generator object.
Q7
What exception is raised when a generator is exhausted?
  • A StopIteration
  • B GeneratorEmpty
  • C NoMoreValues
  • D EndOfGenerator
Answer: Option A
Explanation: StopIteration exception is raised when a generator has no more values to yield.
Q8
Can a generator function have multiple yield statements?
  • A Yes
  • B No
  • C Only in Python 3
  • D Only with special decorators
Answer: Option A
Explanation: Generator functions can have multiple yield statements, and execution resumes from the last yield point.
Q9
What is the output of: (x*x for x in range(3))
  • A A generator object
  • B [0, 1, 4]
  • C (0, 1, 4)
  • D Error
Answer: Option A
Explanation: This generator expression creates squares of numbers 0, 1, 2 but needs to be consumed to see values.
Q10
How can you iterate through all values of a generator?
  • A Using a for loop
  • B Using while True loop
  • C Calling next() repeatedly
  • D All of the above
Answer: Option D
Explanation: Generators can be directly used in for loops, which automatically handle the StopIteration exception.

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