You have a large Python codebase, and you suspect that some parts of the code are suboptimal and slowing down the application. How would you identify and optimize the performance bottlenecks?
- a) Profile the code with a profiler like cProfile
- b) Rewrite the entire codebase from scratch
- c) Ignore the suboptimal code as it may be too time-consuming to fix
- d) Add more hardware resources
To identify and optimize performance bottlenecks in a large codebase, you would profile the code using a profiler like cProfile or more specialized tools like line_profiler or Pyflame. Profiling helps pinpoint which parts of the code are consuming the most time and resources. Rewriting the entire codebase is often impractical. Ignoring suboptimal code can lead to scalability and maintainability issues. Adding more hardware resources can help to some extent, but optimizing the code is a more cost-effective solution.
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.