How can you run a specific test method from a test case in unittest?
- Specify the method name as an argument to unittest.main()
- Use the python -m unittest command
- Use the run method of the test case
- Use the run_test function
To run a specific test method from a test case in unittest, you can use the run method of the test case class and specify the method name as an argument to it. The other options are not the standard way to run specific test methods in unittest.
How can you remove all items from a Python set?
- set.clear()
- set.delete_all()
- set.discard_all()
- set.remove_all()
To remove all items from a Python set, you can use the clear() method. This method clears all elements from the set, leaving it empty. The other options do not exist as methods for removing all items from a set.
How can you reload a module that has been modified after it was initially imported?
- importlib.reload(module)
- module.reload()
- module.reload_module()
- reload(module)
To reload a module in Python, you can use the importlib.reload(module) function from the importlib library. This function reloads the specified module, allowing you to access any changes made to it.
How can you profile a Python script to analyze the time spent in each function call?
- Use the cProfile module to profile the script, which provides detailed information about the time spent in each function call.
- Use the inspect module to analyze the source code of the script.
- Use the timeit module to measure the execution time of the entire script.
- Use the trace module to trace the execution of the script line by line.
Profiling a Python script to analyze function call times involves using the cProfile module, which provides detailed statistics on function calls, including time spent in each function.
How can you prevent overfitting in a deep learning model developed with TensorFlow or PyTorch?
- Decrease the learning rate
- Increase the model complexity
- Use a smaller training dataset
- Use dropout layers
To prevent overfitting, using dropout layers is a common technique. Dropout layers randomly deactivate a fraction of neurons during training, which helps the model generalize better to new data.
How can you perform element-wise multiplication of two NumPy arrays?
- array1 * array2
- array1.multiply(array2)
- np.multiply(array1, array2)
- np.multiply_elements(array1, array2)
To perform element-wise multiplication of two NumPy arrays, you can simply use the * operator between the two arrays. NumPy overloads arithmetic operations to work element-wise.
How can you pass dynamic data from a Python back-end to a JavaScript variable in the front-end?
- Include Python variables directly in JavaScript code.
- Use AJAX requests to fetch data from the Python back-end.
- Use HTTP cookies to store Python data for JavaScript to access.
- Use WebSockets to establish a real-time connection.
To pass dynamic data from a Python back-end to a JavaScript variable, you typically use AJAX (Asynchronous JavaScript and XML) requests. This allows you to make asynchronous requests to the back-end, retrieve data, and update JavaScript variables without refreshing the entire page.
How can you parameterize a test function in pytest to run it multiple times with different arguments?
- Using the @param decorator
- Using the @parametrize decorator
- Using the @pytest.mark.parametrize decorator
- Using the @pytest.parameterize decorator
To parameterize a test function in pytest, you should use the @pytest.mark.parametrize decorator. It allows you to specify multiple sets of input arguments and expected outcomes for a test function.
How can you optimize the speed of a Python program that performs extensive numerical computations?
- Add more comments and documentation to your code.
- Use the print() function extensively to debug your code.
- Utilize specialized libraries like NumPy and optimize your algorithms.
- Write your own mathematical functions from scratch.
To optimize a Python program for numerical computations, you should use specialized libraries like NumPy and focus on optimizing your algorithms. Debugging with print() and adding comments won't directly improve speed.
How can you optimize the recursive Fibonacci function with dynamic programming?
- Convert it to an iterative function
- Implement a tail-recursive version
- Increase the base case value
- Use memoization to store intermediate results
Dynamic programming can optimize the recursive Fibonacci function by using memoization to store previously calculated Fibonacci numbers, reducing redundant calculations. The other options don't directly optimize the recursive approach.
How can you optimize the performance of static files (CSS, JS, Images) in a web application developed using Python frameworks?
- Compress and minify static files
- Optimize database queries
- Use serverless functions
- Utilize a Content Delivery Network (CDN)
Utilizing a Content Delivery Network (CDN) is a highly effective way to optimize the performance of static files. CDNs distribute your files across multiple geographically distributed servers, reducing latency and improving load times.
How would you use a metaclass to automatically register all subclasses of a base class in Python?
- Define a register_subclasses function within the base class.
- Subclasses cannot be automatically registered using metaclasses.
- Use the @register_subclass decorator in conjunction with a metaclass.
- You can use the __init_subclass__ method in a metaclass to automatically register subclasses.
The __init_subclass__ method in a metaclass allows you to automatically register subclasses when they are defined, enabling a way to track and manage them.