You are designing a framework and want to ensure that all classes following a certain API have required methods implemented. How would you use metaclasses to achieve this?
- Metaclasses can automatically inject the required methods into all classes using the API.
- Metaclasses can be used to define a base class with the required methods, and then, for each class, you create a metaclass that checks if these methods are implemented.
- Metaclasses can be used to dynamically create subclasses with the required methods, enforcing the API.
- Metaclasses cannot be used for this purpose.
Metaclasses provide a way to define behaviors for classes. In this scenario, you can define a metaclass that checks if the required methods are implemented when a class is created, ensuring adherence to the API.
You are designing a RESTful API for an e-commerce platform. How would you structure the API endpoints to handle CRUD operations for products?
- a) /api/products/create, /api/products/read, /api/products/update, /api/products/delete
- b) /api/createProduct, /api/readProduct, /api/updateProduct, /api/deleteProduct
- c) /api/products POST, GET, PUT, DELETE
- d) /api/products, POST, GET, PUT, DELETE
In a RESTful API, CRUD operations are typically represented by HTTP methods. Option (c) follows REST conventions with POST, GET, PUT, and DELETE HTTP methods for creating, reading, updating, and deleting products.
You are designing an algorithm to match the opening and closing parentheses in an expression. Which data structure would be suitable for this purpose?
- Array
- Linked List
- Queue
- Stack
A stack is a suitable data structure for matching opening and closing parentheses in an expression. As you encounter opening parentheses, you push them onto the stack, and when you encounter a closing parenthesis, you pop from the stack, ensuring that they match. This approach helps maintain the order of parentheses and is well-suited for this purpose.
You are developing a Django application with a focus on high performance. How would you optimize database queries in views to reduce the load time?
- Implement pagination for large query results
- Increase the number of database queries for more accurate data
- Use Django's built-in caching system
- Use synchronous database calls to ensure real-time data
To optimize database queries and reduce load time, you can utilize Django's built-in caching system. Caching stores frequently used data in memory, reducing the need to repeatedly query the database.
You are developing a Python application and suspect a memory leak. Which tool or technique would you use to identify and analyze the memory consumption?
- a) Manual code review
- b) Python debugger (pdb)
- c) Memory profiling tools
- d) Code optimization
To identify and analyze memory consumption and potential memory leaks, you would use memory profiling tools. These tools, such as memory_profiler or Pyflame, help you monitor memory usage during program execution, making it easier to pinpoint memory leaks. Manual code review and code optimization are not specific to memory leak detection. The Python debugger (pdb) is primarily for debugging code logic, not memory issues.
You are developing a Python application where a certain function’s output is dependent on expensive computation. How would you use decorators to optimize this scenario?
- Create a decorator function that caches the function's output using a dictionary.
- Create a decorator function that logs function arguments and return values.
- Create a decorator function that raises an exception if the function takes too long to execute.
- Create a decorator function that replaces the function with a faster implementation.
To optimize a function with expensive computation, you can use a decorator that caches the function's output, preventing redundant computations. This is known as memoization and is commonly used for optimization.
You are assigned to develop a machine learning model that can identify fraudulent transactions. How would you deal with the class imbalance in the dataset?
- No need to address class imbalance
- Oversampling the minority class
- Removing the imbalance by eliminating records
- Undersampling the majority class
Dealing with class imbalance often involves oversampling the minority class, creating synthetic data points to balance the dataset. This ensures that the model doesn't bias towards the majority class, which is crucial in fraud detection where fraudulent transactions are rare.
You are assigned to implement a complex object creation scenario where the object’s construction process should be separate from its representation. Which design pattern would you use?
- Decorator Pattern
- Factory Pattern
- Prototype Pattern
- Singleton Pattern
The Factory Pattern is used to create objects with a separation between the construction process and the representation. Factories encapsulate object creation, making it easier to change or extend object creation logic.
You are assigned to implement a complex object creation scenario where the object’s construction process should be separate from its representation. Which design pattern would you use?
- Bridge Pattern
- Factory Pattern
- Observer Pattern
- Singleton Pattern
In this scenario, you would use the Factory Pattern. The Factory Pattern separates the object's creation process from its representation, providing a method for creating objects based on certain conditions or parameters. It promotes flexibility and allows for the construction of complex objects.
You are assigned to optimize a Python application performing extensive calculations. Which approach would you take to reduce the computational time and improve the efficiency of the calculations?
- a. Use parallel processing
- b. Increase the screen resolution
- c. Add more memory
- d. Use a different programming language
To reduce computational time and improve efficiency in a Python application with extensive calculations, you should use parallel processing. This involves splitting the calculations into multiple threads or processes to utilize multi-core CPUs. Increasing screen resolution and adding more memory won't directly impact computational efficiency. Switching to a different programming language may not be necessary and can introduce development challenges.