You are required to create a Python module that should expose only specific functions when imported. How would you hide the internal implementation details and expose only the necessary functions?
- a) Use the __all__ attribute
- b) Define functions inside a class
- c) Use double underscores before function names
- d) Create a separate module for each function
To expose only specific functions when importing a Python module, you can define the __all__ attribute at the module level. This attribute is a list of function names that should be considered part of the module's public API, hiding the rest of the implementation details.
You are required to implement a custom iterator that needs to maintain its internal state between successive calls. Which method should you implement in your class to achieve this?
- __init__()
- __iter__()
- __next__()
- __str__()
To create a custom iterator that maintains internal state between successive calls, you should implement the __next__() method in your class. This method defines the logic for generating the next value in the iteration and should raise StopIteration when there are no more items to iterate over.
You are given a list of numbers and you need to find the two numbers that sum up to a specific target. Which algorithmic approach would you use to solve this problem efficiently?
- A) Linear Search
- B) Binary Search
- C) Hashing
- D) Bubble Sort
To efficiently find two numbers that sum up to a specific target, you should use the Hashing approach. This allows you to store elements in a data structure like a hash table or set, which enables constant-time lookup for each element. The other options are not optimal for this task. Linear search and bubble sort are not efficient for this purpose, and binary search assumes the list is sorted.
You are given a task to analyze the correlation between different numerical features in a dataset. Which Pandas method would you use to quickly observe the pairwise correlation of columns?
- .corr()
- .describe()
- .mean()
- .plot()
To quickly observe the pairwise correlation of columns in a Pandas DataFrame, you would use the .corr() method. It calculates the correlation coefficient between all numerical columns, providing valuable insights into their relationships.
You are implementing a caching mechanism. You need a data structure that removes the least recently added item when the size limit is reached. Which built-in Python data structure would you use?
- List
- OrderedDict
- Queue
- Set
An OrderedDict (Ordered Dictionary) is a built-in Python data structure that maintains the order of elements based on their insertion time. It can be used to implement a caching mechanism where the least recently added item can be removed when the size limit is reached.
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.