How would you deploy a Django application to a production environment, considering scalability and security?
- Deploy the Django application without a reverse proxy. Implement security measures within Django views and models. Use a basic firewall.
- Host the application on a shared hosting platform. Use self-signed certificates for SSL/TLS. Deploy only a single server instance. Enable root access for easier management.
- Use a single server with Docker containers for isolation. Disable SSL/TLS for faster performance.
- Use a web server like Nginx or Apache as a reverse proxy in front of Gunicorn or uWSGI. Implement SSL/TLS for secure communication. Utilize a load balancer to distribute traffic across multiple server instances. Harden the server by following security best practices.
Deploying a Django application for production involves multiple steps, including setting up a reverse proxy, securing communications with SSL/TLS, load balancing for scalability, and following security best practices.
How would you design a class that shouldn’t be instantiated?
- By declaring the class as abstract.
- By defining a private constructor.
- By making the class private.
- By using the final keyword.
To prevent a class from being instantiated, you can define a private constructor. When the constructor is private, it cannot be called from outside the class, effectively preventing object creation.
How would you enable Cross-Origin Resource Sharing (CORS) in a Flask application?
- CORS is enabled by default in Flask
- Modify the browser's settings
- Use the "@cross_origin" decorator
- Use the Flask-CORS extension
You can enable CORS in Flask by using the Flask-CORS extension. The other options are not the recommended way to enable CORS in Flask.
How would you enable Cross-Origin Resource Sharing (CORS) in a Flask application?
- Add Access-Control-Allow-Origin header to each route manually.
- CORS is not applicable to Flask applications.
- Set CORS_ENABLED = True in the Flask app configuration.
- Use the @cross_origin decorator from the flask_cors extension.
To enable CORS in a Flask application, you typically use the @cross_origin decorator provided by the flask_cors extension. This allows you to control which origins are allowed to access your API.
How would you ensure that a piece of code in a module is only executed when the module is run as a standalone program and not when it is imported?
- #execute_if_standalone
- #only_run_when_main
- #standalone_code
- if name == "main":
To ensure that a piece of code in a Python module is only executed when the module is run as a standalone program and not when it is imported, you can use the special if __name__ == "__main__": conditional statement. Code inside this block will only run when the module is the main entry point of the program.
How would you find the loop in a linked list?
- Iterate through the list and check for a null reference
- Use a hash table to store visited nodes
- Use a stack to track visited nodes
- Use Floyd's Tortoise and Hare algorithm
Floyd's Tortoise and Hare algorithm is a popular technique to detect a loop in a linked list. It involves two pointers moving at different speeds through the list. If there's a loop, they will eventually meet. The other options are not efficient for loop detection.
How would you find the shortest path in a weighted graph?
- A* Algorithm
- Breadth-First Search
- Depth-First Search
- Dijkstra's Algorithm
Dijkstra's Algorithm is used to find the shortest path in a weighted graph with non-negative edge weights. It guarantees the shortest path but doesn't work with negative weights. Breadth-First and Depth-First Search are used for different purposes, and A* is for finding the shortest path with heuristics.
How would you handle collisions in a hash table?
- Ignore the new value
- Replace the existing value with the new one
- Resize the hash table
- Use linear probing
Collisions in a hash table can be handled by using techniques like linear probing, which involves searching for the next available slot in the table when a collision occurs. This ensures that all values are eventually stored without excessive collisions.
How would you handle large DataFrames that do not fit into memory using Pandas?
- Reducing the precision of data
- Reshaping the DataFrame
- Splitting the DataFrame into smaller chunks
- Using the Dask library
When dealing with large DataFrames that do not fit into memory, you can use the Dask library, which allows for distributed computing and can handle larger-than-memory datasets.
How would you handle missing data for a numerical feature in a dataset before training a machine learning model?
- Ignore missing data, it won't affect the model
- Remove the rows with missing data
- Replace missing values with a random value
- Replace missing values with the mean of the feature
Handling missing data is crucial. Replacing missing values with the mean of the feature is a common practice as it retains data and doesn't introduce bias, especially in numerical features. Removing rows or using random values can lead to loss of information or noise.