You have developed a machine learning model for a recommendation system. What evaluation metric would you use to assess the quality of the recommended items?
- Mean Absolute Error (MAE)
- Mean Average Precision (MAP)
- Precision-Recall Curve
- Root Mean Square Error (RMSE)
In recommendation systems, Mean Average Precision (MAP) is a suitable metric. It considers both the precision and recall of the recommendations, providing a balanced view of the model's performance in suggesting relevant items to users. MAE and RMSE are more appropriate for regression tasks.
You have identified a performance issue in a critical section of your Python code. Which Python profiling tool would you use to analyze the execution time of this code section and identify the bottleneck?
- A. cProfile
- B. PyCharm Debugger
- C. print() statements
- D. PyTest
Profiling tools like cProfile are designed to analyze code performance by measuring execution time and identifying bottlenecks. Option B is a debugger, not a profiler. Option C uses manual print statements, which are not as comprehensive for performance analysis. Option D is a testing framework, not a profiler.
You have to develop a Django app that should be able to handle multiple databases. How would you configure the app to work with multiple databases?
- Create separate Django apps for each database.
- Define multiple database configurations in Django's settings.py, specifying each database's details and then use database routers to route queries to the appropriate database.
- Modify the Django source code to support multiple databases.
- Use a single database configuration for all data to simplify the setup.
In Django, you can configure multiple databases by defining their details in the settings.py file and then using database routers to determine which database to use for specific queries. Option 2 is not suitable for handling multiple databases efficiently. Options 3 and 4 are not recommended approaches.
You have to visualize the frequency distribution of a categorical variable. Which type of plot would you prefer using Matplotlib?
- Bar Plot
- Histogram
- Line Plot
- Scatter Plot
To visualize the frequency distribution of a categorical variable, a bar plot is commonly used in Matplotlib. Each category is represented by a bar, and the height of the bar corresponds to the frequency or count of that category in the dataset.
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.