You are assigned a task to implement a decorator that logs the arguments and return value every time a function is called. How would you implement this logging decorator?
- Define a decorator function that wraps the original function, prints arguments, calls the function, prints return value, and returns the result.
- Modify the function itself to log arguments and return values.
- Use a built-in Python module like logging to log function calls automatically.
- Use a third-party library like Flask to create a logging decorator.
To implement a logging decorator, create a decorator function that wraps the original function, logs the arguments, calls the function, logs the return value, and returns the result. This is a common use case for decorators.
Which Python module is commonly used for writing unit tests?
- debugger
- logging
- pytest
- unittest
The unittest module is commonly used in Python for writing unit tests. It provides a testing framework to create and run test cases and manage test suites. While pytest is another popular testing framework, it's not a module but an external library. debugger and logging are unrelated to writing unit tests.
Which Python module provides a set of functions to help with debugging and interactive development?
- debug
- debugutil
- inspect
- pdb
The Python module pdb (Python Debugger) provides a set of functions for debugging and interactive development. It allows you to set breakpoints, step through code, inspect variables, and more.
Which Python module provides a set of tools for constructing and running scripts to test the individual units of your code?
- assert
- debugger
- sys
- unittest
The unittest module in Python provides a framework for writing and running unit tests. It allows you to create test cases and test suites to verify the correctness of your code's individual units or functions.
Which Python module would you use for logging error and debugging messages?
- debug
- logging
- sys
- trace
The logging module is commonly used for logging error and debugging messages in Python. It provides a flexible and configurable way to manage logs in your applications.
Which Python module would you use for measuring the performance of small code snippets?
- benchmark
- datetime
- profiling
- timeit
You would use the timeit module to measure the performance of small code snippets in Python. It provides a simple way to time small bits of Python code and is a useful tool for optimizing code.
Which Python tool would you use to visualize an application’s call stack and identify performance bottlenecks?
- cProfile
- Gunicorn
- Pyflame
- Pygraphviz
Pyflame is a tool for profiling Python applications. It helps visualize the call stack and identify performance bottlenecks. cProfile (Option 1) is a built-in profiler, but it doesn't offer visualization. Gunicorn (Option 3) is a web server. Pygraphviz (Option 4) is for graph visualization, not profiling.
Which Python web framework uses the “Don’t repeat yourself” principle?
- Django
- Flask
- Pyramid
- Tornado
Django is a Python web framework that follows the "Don't repeat yourself" (DRY) principle. DRY encourages developers to avoid duplicating code by providing reusable components and an organized structure.
Which Seaborn function would you use to visualize a bivariate distribution of two variables?
- sns.barplot()
- sns.distplot()
- sns.jointplot()
- sns.plot()
To visualize a bivariate distribution of two variables in Seaborn, you should use the sns.jointplot() function. It creates a scatter plot with marginal histograms and can also display a regression line or a kernel density estimate.
Which sorting algorithm is best suited for large datasets?
- Bubble Sort
- Insertion Sort
- Quick Sort
- Selection Sort
Quick Sort is typically the best choice for sorting large datasets due to its average-case time complexity of O(n log n). Bubble Sort, Insertion Sort, and Selection Sort have worse time complexities and are less efficient for large datasets.