The recommended way to handle API versioning in RESTful APIs is by including the version in the URL. This ensures backward compatibility and makes it clear which version of the API a client is using. The other options are not considered best practices.

When defining custom exceptions in many programming languages, including Python and Java, it's a best practice to derive them from a built-in exception class like 'Exception.' This allows your custom exception to inherit essential exception handling features.

In the Python Debugger (pdb), you can use the p or print command to display the value of an expression. This is helpful for inspecting variables and understanding program state during debugging.

In the unittest framework, individual test methods should start with the word "test" (e.g., test_something). These methods contain the actual test logic that the test runner executes.

In a balanced Binary Search Tree (BST), inserting an element takes O(log n) time on average. This is because the tree's balanced structure ensures that you traverse down the tree logarithmically with respect to the number of nodes.

When attempting to import a non-existent module in JavaScript, it will silently fail, and no error will be thrown. This is because ES6 modules use static imports, and errors are only raised at runtime when the module cannot be found during the initial load.

Using WebSockets is the most efficient way to handle real-time data updates. WebSockets provide full-duplex communication channels over a single TCP connection, enabling real-time, bidirectional data transfer between the server and client.

Merge Sort is known for its consistent and reliable worst-case time complexity, which is O(n log n) for both average and worst cases. Quick Sort, although efficient in practice, can have a worst-case time complexity of O(n^2) if not implemented carefully.

A Heap is the best data structure to implement a priority queue. Heaps, such as Binary Heaps or Fibonacci Heaps, efficiently maintain the highest-priority element at the top, allowing for quick access and extraction of elements with the highest priority.

The time complexity of inserting an element into a balanced binary search tree (BST) is O(log n), where n is the number of nodes in the BST. In a balanced BST, each insertion or search operation reduces the search space by half, leading to logarithmic time complexity.

Checking membership in a set in Python is an O(1) operation on average because sets use a hash table internally, which provides constant-time access to elements.

Accessing an element in a Python list by index has a time complexity of O(1). Lists are implemented as arrays, and accessing an element by index can be done in constant time because the index directly maps to a memory location.