How can you use the unittest framework to confirm that a specific exception is raised by a piece of code?
- Use assertException() method
- Use assertRaises() method
- Use checkException() method
- Use expectException() method
In unittest, you can use the assertRaises() method to confirm that a specific exception is raised by a piece of code. This method takes the exception class and the callable as arguments and asserts that the callable raises the specified exception.
How can you view the list of variables and their values in the current scope while debugging with Pdb?
- list()
- locals()
- scope()
- vars()
To view the list of variables and their values in the current scope while debugging with Pdb, you can use the locals() function. It returns a dictionary of all local variables in the current scope.
How can you visualize the distribution of a single variable using Seaborn?
- Box plot
- Histogram
- Line plot
- Scatter plot
To visualize the distribution of a single variable in Seaborn, you can use a histogram. A histogram displays the frequency distribution of the data, showing how values are distributed across different bins or intervals.
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 do you convert a list of lists into a single flat list in Python?
- [item for sublist in nested_list for item in sublist]
- list(nested_list)
- nested_list.flat()
- nested_list.flatten()
To flatten a list of lists in Python, you can use a list comprehension with nested loops. This method creates a new list containing all elements from the inner lists concatenated together.
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 do you instantiate an object from a class in Python?
- create Object from Class;
- new Object(Class);
- obj = Class()
- object = new Class()
To instantiate an object from a class in Python, you use the syntax object_name = Class_name(). The other options are not valid syntax for object instantiation in Python.
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 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 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.