You are tasked with integrating a Python back-end with a complex front-end application developed using React. How would you structure the communication between the front-end and the back-end to ensure scalability and maintainability?
- Embed Python code directly into React components for performance.
- Implement a RESTful API with proper authentication and versioning.
- Store all data in local storage for rapid access.
- Use AJAX for direct client-to-server communication.
Implementing a RESTful API with proper authentication and versioning is a scalable and maintainable approach. It allows for structured communication between the front-end and back-end while maintaining flexibility and security.
You are tasked with optimizing a Python application that processes large amounts of data and is running out of memory. Which technique would you use to manage memory more efficiently?
- a. Implement lazy loading
- b. Increase RAM
- c. Use a more memory-efficient data structure
- d. Optimize the CPU
To manage memory more efficiently in a Python application processing large data, you can implement lazy loading. This means loading data into memory only when it's needed, reducing the overall memory consumption. Increasing RAM might not always be possible or cost-effective, and optimizing the CPU won't directly address memory issues. Using memory-efficient data structures is a good practice but might not be sufficient in all cases.
You are tasked with optimizing a RESTful API that experiences high traffic and heavy load. Which caching mechanism would be most appropriate to reduce server load and improve response times?
- a) Client-side caching
- b) Server-side caching
- c) Database caching
- d) Cookie-based caching
For optimizing a RESTful API under heavy load, server-side caching is the most appropriate choice. It stores responses on the server and serves them to subsequent requests, reducing the load on the API and improving response times.
You are required to implement a feature where you need to quickly check whether a user's entered username is already taken or not. Which Python data structure would you use for storing the taken usernames due to its fast membership testing?
- Dictionary
- List
- Set
- Tuple
A set is the appropriate Python data structure for quickly checking membership (whether a username is already taken or not). Sets use hash-based indexing, providing constant-time (O(1)) membership testing, which is efficient for this scenario.
You are required to implement a Python loop that needs to perform an action after every iteration, regardless of whether the loop encountered a continue statement during its iteration. Which control structure would you use?
- do-while loop
- for loop
- try-catch block
- while loop
To perform an action after every iteration, including those with a continue statement, you should use a do-while loop. This loop structure guarantees that the specified action is executed at least once before the loop condition is evaluated.
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 required to build a Python generator that produces a sequence of Fibonacci numbers. How would you implement the generator to yield the Fibonacci sequence efficiently?
- Create a list of all Fibonacci numbers and return it as a generator.
- Implement the generator using a recursive approach to calculate Fibonacci numbers.
- Use a loop to generate Fibonacci numbers and yield them one by one.
- Use a stack data structure to generate Fibonacci numbers efficiently.
To generate Fibonacci numbers efficiently, you should use a loop and yield each Fibonacci number one by one. The recursive approach (Option 1) is inefficient due to repeated calculations.
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.