In PHP, to perform a pattern match using a Regular Expression, you can use the preg_match() function where the first argument is the ______ and the second argument is the string to search within.
- Regular Expression
- Target string
- Pattern modifier
- Replacement string
In PHP, to perform a pattern match using a Regular Expression, you can use the preg_match() function. The first argument passed to preg_match() is the Regular Expression pattern itself. The second argument is the target string or the string within which you want to search for a match. The preg_match() function returns true if the pattern is found within the target string, and false otherwise. It is a powerful function that allows you to search, extract, and manipulate data based on specific patterns defined by Regular Expressions. Learn more: https://www.php.net/manual/en/function.preg-match.php
What are some common uses of the $_SESSION superglobal array in PHP?
- Storing user data
- Tracking user activity
- Implementing shopping carts
- Maintaining user preferences
- All the options
The $_SESSION superglobal array in PHP is commonly used for various purposes. It allows storing user-specific data, tracking user activity across different pages, implementing shopping carts, and maintaining user preferences throughout the session. It provides a way to persistently store and retrieve data specific to a user's session. Refer to: http://php.net/manual/en/reserved.variables.session.php
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 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 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 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 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.
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.