ExamAdept

How can you implement a generator that produces a Fibonacci sequence?

A:

Using a loop with a yield statement and two variables

B:

By calling a recursive function with yield

C:

Using a list comprehension with yield

D:

By using the itertools.cycle() function

Answer: A

A generator for a Fibonacci sequence can be implemented using a loop with a yield statement and two variables to keep track of the current and previous values. -PROGCODE-

def fibonacci_sequence():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b

# Example usage
fibonacci_gen = fibonacci_sequence()
for _ in range(5):
    print(next(fibonacci_gen))

-PROGCODE-END-

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What is the purpose of the itertools.starmap() function when used with generators?

A:

It applies a function to elements of the generator

B:

It generates the sum of elements in the generator

C:

It stops the generator after one iteration

D:

It filters elements based on a specified condition

Answer: A

itertools.starmap() applies a function to elements of the generator, using arguments unpacked from each tuple in the input iterable.

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What happens if a generator function contains both yield and return statements?

A:

It raises a GeneratorExit exception

B:

It raises a StopIteration exception

C:

It returns the specified value and stops the generator

D:

It is a syntax error; yield and return cannot be used together

Answer: C

If a generator function contains both yield and return statements, it returns the specified value and stops the generator.

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How does the generator.throw(RuntimeError, "message") method affect a running generator?

A:

It raises a custom exception inside the generator

B:

It forcibly terminates the generator

C:

It has no effect on the running generator

D:

It restarts the generator from the beginning

Answer: A

generator.throw(RuntimeError, "message") raises a custom exception of type RuntimeError with the specified message inside the generator.

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What is the purpose of the itertools.product() function when used with generators?

A:

It generates the Cartesian product of input iterables

B:

It filters elements based on a specified condition

C:

It stops the generator after one iteration

D:

It interleaves values from different generators

Answer: A

itertools.product() generates the Cartesian product of input iterables, creating tuples with all possible combinations of elements.

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Questions for: Generators