Graded Questions

Question 1

Which of the following is NOT a valid way to iterate through a generator in python?

Using a for loop

Using the next() function.

Using a while loop

Using the yield statement.

Question 2

What is the purpose of the itertools. islice() function in python?

It creates a list from an iterator.

It returns a slice of an iterable, such as a generator.

It returns the first item of an iterable.

It reverses an iterable.

Question 3

Which of the following is true about the yield statement and memory usage?

yield statement increases memory usage significantly.

yield statement decreases memory usage significantly.

yield statement has no effect on memory usage.

Memory usage depends on the implementation of the generator.

Question 4

How do you define a generator function in python 2?

Using the generator keyword.

By simply defining a function with a yield statement.

By using a special decorator.

Generators are not supported in python 2.

Question 5

How can you iterate through multiple generators in parallel in python?

Use the next() function on each generator.

Use a for loop on each generator separately.

Use the itertools.chain() function.

Use the itertools.zip_longest() function.

Question 6

How can you create a generator that produces numbers from 1to10?

Using a range() function.

By specifying a list of values in a generator expression.

By defining a generator function with a loop.

You can't generate numbers from 1 to 10 using a generator.

Question 7

What does the itertools. cycle() function do in python?

It calculates the circumference of a circle.

It creates an infinite generator that cycles through a specified iterable.

It performs cyclic permutations on an iterable.

It generates random numbers. makefile

Question 8

What happens if you attempt to use the return statement in a generator function?

It returns a value and terminates the generator.

It raises a stopIteration exception.

It has no effect, and the generator continues running.

It triggers a runtime error.

Question 9

Which of the following is NOT a valid use case for generator functions in python?

Generating values for a mathematical series.

Reading and processing a large log file line by line.

Generating HTML code.

Running complex mathematical calculations.

Question 10

What does the itertools.zip_longest() function do?

It generates pairs of elements from multiple iterables until the shortest one is exhausted.

It creates a zip file from multiple files.

It generates an infinite sequence of pairs.

It raises a zipException when iterables are of unequal lenght.

Question 11

How can you stop the execution of a generator before it's exhausted?

Raise a GeneratorExit exception in the generator.

Call the close() method on the generator.

Use the yield keyword with an explicit value to signal the end.

You can't stop a generator prematurely.

Question 12

Which of following is NOT a valid use case for generators in python?

Iterating over a large dataset that does't fit in memory.

Implementing custom context managers.

Generating random numbers.

Implementing lazy sequences and pipelines.

Question 13

Which of the following is a built-in function for creating a generator from an existing iterable?

itertools.create()

itertools.generate()

itertools.from_iterable()

itertools.islice()

Question 14

What does the itertools . tee() function do in python?

It creates a copy of an iterable.

It generates an infinite sequence of values.

It duplicates a generator into multiple independent generators.

It filters an iterable.

Question 15

Which of the following is a way to create an infinite generator in python?

Use a for loop with no exit condition.

Define a generator function that never contains a yield statement.

Use the yield statement with an infinite loop.

Use the stop() method.

Question 16

Which of the following statements is true about the yield keyword?

It terminates the function and returns a value to the caller.

It defines a new generator object.

It suspends the function's state and returns a value, allowing the function to be resumed.

It raises an exception when used in a function.

Question 17

What happens when a generator raises a StopIteration exception explicitly?

The program crashes with an error.

The generator is considered exhausted.

The generator continues execution.

The generator is closed automatically.

Question 18

What is the purpose of the itertools.islice() function?

It creates a new iterable.

It returns a slice of an iterable without consuming the original iterable.

It reverses an iterable.

It shuffles the elements of an iterable.

Question 19

How can you convert a generator to a list in python?

Use the list() constructor.

Call the to_list() method on the generator.

Use a list comprehension.

Generators cannot be converted to lists.

Question 20

What is the purpose of the generator.close() method in python?

To close the generator permanently.

To generate an exception.

To stop the generator execution.

To restart the generator.

Question 21

How do you raise a custom exception when a generator is exhausted?

Use the raise statement in the generator function.

Call the close() method whit a custom exception.

You can't raise a custom exception when a generator is exhausted.

Use the try...except block to catch the StopIteration exception and raise a custom one.

Question 22

Which of the following statements is true about generator expressions in python?

They are only used for defining regular functions.

They are equivalent to list comprehensions, but they evaluate lazily.

They can only generate integers.

They are deprecated in python 3.

Question 23

How do you create a generator in python?

By using the generate keyword.

By defining a regular function.

By using the yield keyword in a function.

By using the gen() constructor.

Question 24

How do you implement a generator function that produces a sequence of unique random numbers between 1 and 100?

Use a loop with random number generation and yield values.

Use a generator expression with random numbers.

Use a list comprehension with random numbers.

Generators cannot produce random numbers.

Question 25

What does the next() function do with a generator?

It generates the next random number.

It returns the next value produced by the generator.

It restarts the generator from the beginning.

It raises a stopIteration exception when the generator is exhausted.

Question 26

Which of the following is NOT a valid way to create a generator in python?

Using a generator expression.

Defining a generator function using def.

Converting a list to a generator using list().

Using the yield statement in a function.

Question 27

When is a generator considered exhausted?

When the yield keyword is used.

When the generator is defined.

When the StopIteration exception is raised.

When there are no more values to yield.

Question 28

What happens when you call a generator function in python?

It immediately executes and returns all the values.

It raises a GeneratorError exception.

It returns a generator object without executing the function.

It raises a StopIteration exception.

Question 29

Which of the following methods is used to get the current value of a generator without advancing to the next one?

next()

peek()

current()

There is no built-in method for this.

Question 30

Which method is used to define a generator in a more concise manner in python?

generate

generator

yield

genexp

Question 31

How do you iterate through a generator using a for loop in python?

You can't use a for loop with generators.

Use the yield statement to generate values.

Simply pass the generator to the for loop, which automatically calls next() until exhaustion.

Use the return statement to retrieve values.

Question 32

What is a generator function in python?

A device that produces electrical power.

A special type of iterable that generates values on-the-fly.

A function that generates random numbers.

A built-in module in python.

Question 33

What is a benefit of using generator expressions over list comprehensions?

Generator expressions are more concise.

Generator expressions generate lists.

List comprehensions are evaluated lazily.

Generator expressions consume more memory.

Question 34

What is the primary advantage of generators in python?

They generate random values efficiently.

They allow you to create infinite loops.

They can produce values lazily, conserving memory.

They simplify error handling in code.

Question 35

What is the purpose of the yield from statement in a generator?

It is used to yield a single value from a nested generator.

It is used to yield a sequence of values from a nested generator.

It is used to define a generator within a generator.

It is not a valid statement in python.

Question 36

What is the primary use of the yield statement in a generator?

To generate a random value.

To terminate the generator.

To yield a value and save the generator's state.

To create an infinite loop.

Question 37

What is the purpose of the itertools.accumulate() function in python?

It calculates the cumulative sum of elements in an iterable.

It accumulates elements in a list.

It generates an infinite sequence of values.

It performs element-wise addition on multiple iterables.

Question 38

How do you create a generator that produces Fibonacci numbers using a generator function?

Define a recursive generator function.

Use a loop inside the generator function.

Implement it as a list comprehension.

You can't create a generator for Fibonacci numbers.

Question 39

What does the itertools.count() function do in python?

It counts the number of elements in an iterable.

It creates an infinite generator that counts from a specified start value.

It counts occurrences of a specified value in an iterable.

It counts the number of times a generator has been iterated.

Question 40

What is the purpose of the send() method in a generator?

It sends a value to the generator and resumes its execution.

It terminates the generator.

It is used to receive values from the generator.

It is not a valid method for generators.

Question 41

What is the primary use case for the itertools.filterfalse() function in python?

Filtering elements from an iterable based on a condition.

Creating a filter for image processing.

Filtering elements from a list.

Generating random values.

Question 42

What happens when a generator function encounters a return statement with a value?

It raises a SyntaxError.

It returns the value and terminates the generator.

It continues execution as if nothing happened.

It raises a GeneratorExit exception.

Question 43

What is the primary difference between a generator and a regular function that returns a list of values?

A generator returns a list, while a regular function returns an iterator.

A generator computes values lazily, while a regular function computes all values at once.

A generator can only be used in a for loop, while a regular function can be used in any loop.

There is no difference; both are used to return values.

Question 44

What is the primary purpose of the itertools.groupby() function in python?

Grouping elements in a list.

Grouping elements in a dictionary.

Grouping elements in an iterable based on a key function.

Generating random groups of elements.

Question 45

What happens when you use the yield statement with an expression, like yield x in a generator?

It generates the value of x immediately.

It raises a SyntaxError

It stores x as the default value for the next yield operation.

It is not allowed in python.

Question 46

What does the itertools.chain() function do in python?

It creates a chain of generators.

It concatenates multiple iterables into one iterable.

It generates random numbers in a chain.

It filters elements from an iterable.

Question 47

How can you create a generator that iterates over the characters of a string?

Use a list comprehension.

Use a for loop.

Define a generator function with a loop.

Strings cannot be converted to generators.

Question 48

Which of the following is true about the yield statement inside a generator function?

It can only yield values of the same data type.

It can yield values of different data types.

It can yield values indefinitely without consuming memory.

It can't be used inside a generator function.

Question 49

How can you create a generator that iterates over the keys of a dictionary?

Use a for loop on the dictionary.

Use the itertools.keys() function.

Define a generator function that yields dictionary keys.

Dictionaries cannot be converted to generators.

Question 50

Which of the following statements is true regarding generator functions and regular functions in python?

Generator functions can only be called once, while regular functions can be called multiple times.

Generator functions are called using the generate keyword.

Regular functions use the yield keyword to return values.

Regular functions always consume less memory than generator functions.

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