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 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.
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 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 developing a Python program and need to debug a function in a module. Which Python tool would you use to step through the code and inspect the values of variables?
- a) print statements
- b) PyCharm
- c) pdb (Python Debugger)
- d) Python Profiler
To step through code, inspect variables, and debug Python programs, you would use the pdb module, which stands for Python Debugger. It allows you to set breakpoints, step into functions, and examine the state of your program during execution.
You are developing a RESTful API and need to ensure that sensitive user data is secure during transit. Which approach would you use to secure data transmission?
- a) Encoding data as plain text
- b) Using HTTPS (SSL/TLS)
- c) Encrypting data with a custom algorithm
- d) Adding authentication tokens to requests
The most secure approach for securing data in transit in a RESTful API is to use HTTPS (SSL/TLS). It encrypts the data between the client and server, providing confidentiality and integrity.