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Python Generators and Decorators Skills Test

Assess your understanding of Python's generator functions for lazy evaluation and decorators for modifying function behavior. This test covers key concepts like yield, generator expressions, and common decorator patterns.

20–30 Questions per assessment
15–45 min Estimated completion time
3 levels Choose your difficulty
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Answer without outside help so the result reflects your current knowledge. You will see your score after completing the selected assessment.

Mastering generators and decorators is crucial for writing efficient, readable, and Pythonic code. Generators allow memory-efficient iteration, while decorators enable clean code reuse for cross-cutting concerns like logging, timing, and access control.

This is a demo version of the test. You may attempt up to 3 questions.

Test details

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Review the instructions, covered skills, example question themes, and intended audience before beginning.

01

Instructions and covered skills

Read each question carefully and select the best answer. Do not use any external resources or refer to documentation during the test. Manage your time wisely; allocate enough time for each question. Avoid distractions by turning off notifications and finding a quiet environment. Stay focused on the code snippets and think logically. Ensure you understand Python syntax and semantics before answering.

Key Areas

Generators: creation, yield, generator expressions, lazy evaluation, memory efficiency, and use cases like infinite sequences and pipelines.

Decorators: syntax, nested functions, closures, common decorators like @staticmethod, @classmethod, @property, and writing custom decorators with and without arguments.

Recommended Preparation

Practice writing simple generator functions and using them in for loops. Understand how decorators can modify function behavior without changing the source code. Review examples of decorators for logging, timing, and memoization.

02

Examples of questions

1. Which keyword is used to yield a value from a generator?
2. What is the primary advantage of using a generator over a list when dealing with large datasets?
3. Which of the following correctly creates a generator expression?
4. In Python, a decorator is a function that ________.
5. Which built-in decorator is used to define a method that does not receive an implicit first argument?
6. How can you pass arguments to a decorator?
7. What does the @property decorator allow a method to be accessed as?
8. What does the 'yield from' statement do in a generator?
9. Once a generator function has yielded all its values, what happens when the next() function is called again?
10. Which of the following is a common use case for decorators?
03

Who this test is best for

Python developers who want to advance their code efficiency and design patterns.

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