Python Generators & Iterators - Practice Questions 2026

Name: Python Generators & Iterators - Practice Questions 2026
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Course Description

Mastering memory efficiency and high-performance coding in Python requires a deep understanding of Generators and Iterators. This practice exam course is specifically designed to bridge the gap between theoretical knowledge and practical application. Whether you are preparing for a technical interview or looking to optimize large-scale data processing scripts, these practice questions provide the rigorous testing environment you need.

Why Serious Learners Choose These Practice Exams

Serious learners prioritize depth and accuracy. Unlike standard quizzes, these practice exams focus on the inner workings of Python’s iteration protocol. By enrolling in this course, you are choosing:

Comprehensive Coverage: Every nuance of the iterator protocol and generator expressions is tested.

Performance Focus: Questions emphasize why and when to use generators to save memory.

Detailed Analytics: Identify exactly where your knowledge gaps lie before you write a single line of production code.

Course Structure

The course is organized into six logical levels to ensure a smooth learning curve:

Basics / Foundations: This section covers the fundamental definitions. You will be tested on the difference between iterables and iterators, the iter() and next() functions, and the basic syntax of generator functions.

Core Concepts: Here, we dive into the yield keyword. You will explore how Python pauses and resumes function execution, and how the state is maintained across iterations.

Intermediate Concepts: This module focuses on Generator Expressions and their comparison to List Comprehensions. It also introduces the StopIteration exception and how it is handled internally by loops.

Advanced Concepts: Challenge yourself with complex topics like the send(), throw(), and close() methods. You will learn about coroutines and how to manage bidirectional data flow within a generator.

Real-world Scenarios: Apply your knowledge to practical problems, such as reading massive log files without crashing memory, streaming data from APIs, and creating custom infinite sequences.

Mixed Revision / Final Test: A comprehensive set of exams that pull questions from all previous sections to simulate a real-world coding assessment or interview environment.

Sample Practice Questions

Question 1

What will be the output of the following code snippet?

Python

def my_gen():

yield 1

yield 2

g = my_gen()

print(next(g))

print(next(g, 3))

Option 1: 1, 2, 3

Option 2: 1, 2, StopIteration

Option 3: 1, 2, None

Option 4: 1, 2, 1

Option 5: Error: next() takes only one argument

Correct Answer: Option 1

Correct Answer Explanation: The next() function accepts an optional second argument which serves as a default value. When the generator g is exhausted after the second yield, the third next(g, 3) call returns the default value 3 instead of raising a StopIteration exception.

Wrong Answers Explanation: * Option 2: This would be correct if the third call was next(g) without a default value.

Option 3: The default value provided was 3, not None.

Option 4: There is no logic in the generator that would reset it to return 1 again.

Option 5: next() is built to take two arguments: the iterator and the default value.

Question 2

How does a Generator Expression differ from a List Comprehension in terms of memory?

Option 1: Generator expressions store all elements in RAM immediately.

Option 2: List comprehensions use "lazy evaluation" to save memory.

Option 3: Generator expressions return a generator object and produce items on demand.

Option 4: There is no difference in memory usage between the two.

Option 5: List comprehensions are always faster regardless of the data size.

Correct Answer: Option 3

Correct Answer Explanation: Generator expressions use lazy evaluation. They do not calculate the value of the items until they are specifically requested. This makes them highly memory efficient for large datasets.

Wrong Answers Explanation:

Option 1: This describes the behavior of a List Comprehension, not a Generator.

Option 2: List comprehensions use eager evaluation, meaning they calculate everything upfront.

Option 3: This is incorrect because List comprehensions scale linearly with data size in terms of memory, whereas Generators remain constant.

Option 5: While List comprehensions can be faster for small datasets due to less overhead, they can cause system crashes on very large datasets due to memory exhaustion.

Course Features

Welcome to the best practice exams to help you prepare for your Python Generators & Iterators journey.