In Python, when multiple base classes have methods with the same name, the method resolution follows the Method Resolution Order (MRO) to determine which method to call.

When a Python function is CPU-bound, the most effective optimization is usually to optimize the algorithm or use more efficient data structures. JIT compilation (a) can help in some cases, but it may not be as effective as algorithmic improvements. Increasing the number of threads (b) might help if the code can be parallelized, but this is not always the case. Using a faster computer (d) is generally not a solution to CPU-bound code as it doesn't address the underlying inefficiencies.

To investigate memory leaks in a Python application, you can use a memory profiler like memory_profiler, which tracks memory usage over time, helping you identify areas of concern. Manual inspection (Option 3) is impractical for large codebases, and psutil (Option 2) primarily focuses on CPU usage. The timeit module (Option 4) measures execution time, not memory usage.

To initialize an empty list in Python, you use square brackets []. Option 2 initializes an empty dictionary, option 3 initializes a variable as None, and option 4 initializes a list with a single element, which is None.

B. Implementing a token bucket algorithm is a common method for rate limiting in RESTful APIs. It allows you to control the rate of requests over time, preventing abuse while allowing legitimate usage. Options A, C, and D are not effective methods for rate limiting.

In Python, you can implement a stack using a list. Lists provide built-in methods like append() and pop() that make it easy to simulate a stack's behavior.

A dequeue can be efficiently implemented using a doubly linked list, where you can add or remove elements from both ends in constant time. While arrays are also used, they may not provide the same level of efficiency for both ends' operations.

In Python, the syntax def function_name(*args) is used to define a function that can take an arbitrary number of positional arguments. The *args syntax allows you to pass a variable number of arguments to the function.

In Python, you use the dot notation (.) to access the attributes and methods of a class. For example, object.attribute or object.method().

In pytest, the @pytest.skip marker is used to skip a test function when certain conditions are met. This allows you to selectively skip tests based on runtime conditions or configurations.

In pytest, the @pytest.config fixture is used to pass command-line options and configuration values to test functions. This allows you to customize the behavior of your tests based on configuration settings.

In pytest, the teardown fixture is used to execute finalization code after the test function has completed its execution. This allows you to perform cleanup tasks or teardown actions after the test has run, such as closing a database connection or cleaning up temporary files.