Python Skill Assessment test with Answers

A sample Python skill-based assessment test includes questions and tasks that evaluate a candidate's understanding of Python, coding proficiency, problem-solving ability, and use of libraries and frameworks.

The test may cover basic to advanced topics depending on the level of the candidate being assessed (junior, mid-level, senior) and the specific role (software developer, data scientist, automation engineer, etc.)

Sample Python question-based skill-based assessment with Expected Answers

Here are some key questions with expected answers assessing the candidate's proficiency in Python and problem-solving skills. 

1. Can you explain the difference between list, tuple, and set in Python? When would you use each?

A list is an ordered, mutable (changeable) collection of items. Lists can contain duplicate elements and can store items of different types. It is used when you need a collection of items that can be changed or modified, need to maintain order of elements, and store duplicate values.

A tuple is an ordered, immutable collection of items. Like lists, tuples can also store items of different types and allow duplicates. It is used when you need an ordered collection that should not be changed to ensure data integrity (e.g., coordinates, fixed data) and to use the collection as a dictionary key (since tuples are hashable, unlike lists).

A set is an unordered, mutable collection of unique items. Sets do not allow duplicate elements and do not maintain any specific order. It is used when you need a collection of unique items and when the order of elements is not important.

2. What is the difference between deep copy and shallow copy in Python?

  • Shallow copy: Creates a new object but copies references of nested objects. Changes to mutable objects in the original will affect the copy.
  • Deep copy: Creates a new object and recursively copies all objects, making them independent of the original.

Example: Using copy.copy() for shallow copy and copy.deepcopy() for deep copy from the copy module.

3. How does Python manage memory? What are some ways to manage memory effectively in Python?

  • Python uses a memory manager, including a built-in garbage collector that handles unused objects.
  • Memory can be managed through:
    • Avoiding circular references.
    • Using generators for large data sets instead of storing entire lists in memory.
    • Using memory-efficient libraries like NumPy for large numerical computations
  1. How would you improve the performance of a Python program?
  • Identify and optimize bottlenecks using tools like cProfile.
  • Use list comprehensions instead of loops for better performance.
  • Optimize large datasets by using generators.
  • Replace inefficient algorithms with more optimal ones (e.g., O(log n) over O(n)).
  • Use external libraries like NumPy for performance-critical tasks.
  1. What is a lambda function, and when would you use it?
  • A lambda function is an anonymous, short function defined with the lambda keyword.
  • It is used when a small function is needed temporarily, especially for sorting, filtering, or mapping.