You are tasked to develop a Flask application that requires user authentication. How would you implement user authentication in a secure manner?
- Implement custom authentication from scratch without any external libraries.
- Store user credentials in plain text in the database.
- Use a well-established authentication library like Flask-Login, Flask-Security, or Flask-Principal.
- Use JavaScript for authentication.
To implement secure user authentication in a Flask application, it's advisable to use established authentication libraries that have been thoroughly tested for security vulnerabilities. Storing passwords in plain text (Option 2) is a security risk, and implementing custom authentication (Option 3) is error-prone. Using JavaScript (Option 4) for authentication is not recommended for security reasons.
You are tasked with creating a predictive model to forecast stock prices. Which type of machine learning model would be most appropriate for this task?
- Convolutional Neural Network
- Decision Tree
- K-Means Clustering
- Linear Regression
Linear Regression is commonly used for predicting continuous values, such as stock prices. It models the relationship between the independent variables and the dependent variable (stock price) through a linear equation. Other options are not suitable for this prediction task.
You are tasked with debugging a large and complex Python application that has multiple modules and classes. How would you systematically approach the debugging process to identify and isolate the issue?
- A. Use console.log() statements throughout the code to print variable values at various points.
- B. Start from the top of the code and work your way down, fixing issues as they arise.
- C. Employ a systematic method such as divide and conquer, where you isolate modules, identify potential issues, and progressively narrow down the problem area.
- D. Rely on automated debugging tools exclusively to find and fix issues.
Debugging a complex application requires a systematic approach. Option C is the correct approach as it involves isolating modules, identifying potential problems, and narrowing down the issue. Option A is helpful but not systematic. Option B is inefficient and may not address root causes. Option D may not be sufficient for complex issues.
You are tasked with designing a class structure where some classes share some common behavior but also have their unique behaviors. How would you design such a class structure?
- Use closures to encapsulate common behavior
- Use inheritance to create a base class with common behavior and derive specialized classes from it
- Use interfaces to define common behavior and have classes implement those interfaces
- Use mixins to mix common behavior into different classes
Mixins are a common design pattern in JavaScript for sharing common behavior among classes. You can create mixins that contain common methods and then mix them into different classes to give them that behavior.
You are tasked with designing a class structure where some classes share some common behavior but also have their unique behaviors. How would you design such a class structure?
- Use Composition
- Use Encapsulation
- Use Inheritance
- Use Polymorphism
To design a class structure where some classes share common behavior but also have unique behavior, you would use Composition. Composition involves creating objects of one class within another class, allowing you to combine the behavior of multiple classes while maintaining flexibility for unique behaviors.
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.