When should you consider using HTTP long-polling in designing RESTful APIs?
- A. When real-time updates are required
- B. For caching static content
- C. To minimize latency in request-response cycles
- D. When working with large file uploads
A. HTTP long-polling is suitable when you need real-time updates from the server. It involves keeping a connection open until new data is available, making it suitable for applications like chat or notifications. Options B, C, and D are not appropriate use cases for long-polling.
When using decorators, what is the significance of the functools.wraps function?
- It is not related to decorators.
- It is used to define custom decorators.
- It is used to wrap a function multiple times.
- It's used to create a wrapper function that preserves metadata like function name and docstring when decorating a function.
The functools.wraps function in Python is often used when creating decorators. It helps preserve the metadata of the original function (e.g., name and docstring) when decorating it with another function. This ensures that the decorated function retains its identity and documentation.
When using Python’s PDB, the command ____ is used to step into a function call.
- next
- step
- step_in
- step_into
In Python's PDB (Python Debugger), the step_into command is used to step into a function call during debugging. It allows you to go into the called function and debug its execution.
When using Scikit-learn, what is the initial step to perform before fitting a model to the dataset?
- Import the required functions and classes
- Install Scikit-learn
- Normalize the data
- Split the data into training and testing sets
The initial step when using Scikit-learn is to import the necessary functions and classes from the library. This allows you to access the machine learning models and tools you need for data preprocessing, model training, and evaluation.
What would be the best sorting algorithm to use if you are concerned about worst-case time complexity?
- Bubble Sort
- Merge Sort
- Quick Sort
- Selection Sort
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.
What would be the most efficient way to handle real-time data updates between a Python back-end and a front-end application?
- Poll the server at regular intervals
- Use HTTP long polling
- Use WebSockets
- Utilize RESTful APIs
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.
What would be the result of attempting to import a module that does not exist?
- It will prompt you to create the module.
- It will silently fail, and no error will be thrown.
- You will get a runtime error, and the program will crash.
- You will receive a warning but the program will continue running.
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.
What would be the time complexity of inserting an element in a balanced Binary Search Tree?
- O(1)
- O(log n)
- O(n log n)
- O(n)
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.
What is the time complexity of inserting an element into a balanced binary search tree?
- O(1)
- O(log n)
- O(n log n)
- O(n)
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.
What would be the best data structure to implement a priority queue?
- Heap
- Linked List
- Queue
- Stack
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.