When designing a RESTful API, how should you handle versioning of the API?
- Do not version the API
- Hardcode the version in the API endpoints
- Include the version in the URL (e.g., /v1/resource)
- Use a header like "X-API-Version"
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 implementing a recursive algorithm, what is crucial to avoid infinite recursion?
- A base case
- A high stack size
- A loop
- Global variables
In a recursive algorithm, it's crucial to have a base case that defines when the recursion should stop. Without a base case, the algorithm will keep calling itself indefinitely, leading to infinite recursion and a stack overflow error.
When integrating a Python back-end with a front-end form, how can you secure the application against Cross-Site Request Forgery (CSRF) attacks?
- Disable JavaScript to prevent malicious form submissions.
- Use a unique token with each form submission and verify it on the server.
- Use HTTPS to encrypt form data.
- Validate user input on the front-end before submission.
To secure an application against CSRF attacks, you should use a unique token (CSRF token) with each form submission. This token is generated on the server and verified on the server to ensure that the request is legitimate and not forged by a malicious attacker.
When optimizing Python code, why is it essential to consider the algorithmic complexity of your solutions?
- Algorithmic complexity only matters for other programming languages, not Python.
- It helps you write faster code by choosing algorithms that have better time complexity.
- It makes your code longer and more complex.
- It's not necessary; you can optimize code without worrying about algorithmic complexity.
Considering algorithmic complexity is crucial because it determines how efficiently your code runs as the input size grows. Choosing algorithms with better time complexity can significantly improve performance.
When preprocessing data, the ____ class in Scikit-learn is used to encode categorical features as a one-hot numeric array.
- LabelEncoder
- OneHotEncoder
- PCA
- StandardScaler
In Scikit-learn, the OneHotEncoder class is used to transform categorical features into a one-hot encoded numeric array. This process is essential when working with machine learning algorithms that require numeric input, as it converts categorical data into a format that the algorithms can understand.
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.
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.
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.