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 required to run a specific test function against multiple sets of inputs and want to ensure that the test runner identifies each set as a separate test. How would you accomplish this in pytest?
- Define multiple test functions with unique names
- Use parameterized testing with @pytest.mark.parametrize
- Use test fixtures with @pytest.fixture
- Utilize test classes and inheritance
To run a test function with multiple sets of inputs, you can use parameterized testing in pytest with @pytest.mark.parametrize. This decorator allows you to specify multiple input sets and ensures that each set is treated as a separate test.
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 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.