How would you test a function that does not return a value, but prints something out, using unittest?
- Manually check the printed output during testing.
- Redirect the printed output to a file and compare the file contents in the test case.
- This cannot be tested with unittest as it's impossible to capture printed output.
- Use the unittest.mock library to capture the printed output and compare it to the expected output.
To test a function that prints something without returning a value, you can use the unittest.mock library to capture the printed output and then compare it to the expected output in your test case. This allows you to assert that the function is producing the expected output.
How would you split a dataset into training and testing sets using Scikit-learn?
- dataset_split(data, 0.2)
- split_data(data, train=0.8, test=0.2)
- train_and_test(data, test_ratio=0.2)
- train_test_split(data, test_size=0.2)
You would use the train_test_split function from Scikit-learn to split a dataset into training and testing sets. It's a common practice in machine learning to use an 80-20 or 70-30 train-test split to evaluate model performance. The other options are not valid functions in Scikit-learn.
How would you set up a custom command in Django that can be run using the manage.py file?
- a. Create a Python script with your command logic, save it in the Django project directory, and add an entry in the commands list in the project's __init__.py.
- b. Create a Python script with your command logic and place it in the management/commands directory of your Django app.
- c. Modify the Django source code to add your custom command.
- d. Use a third-party package for custom commands.
To set up a custom management command in Django, you should create a Python script in the management/commands directory of your app. Django will automatically discover and make it available through manage.py. Options a, c, and d are not standard practices.
How would you set a breakpoint in a Python script to start debugging?
- breakpoint()
- debug()
- pause()
- stop()
In Python 3.7 and later, you can set a breakpoint by using the breakpoint() function. It pauses the script's execution and enters the interactive debugger at that point, allowing you to examine variables and step through code.
How would you run a Python script from the command line and pass arguments to it?
- python execute script.py with-args arg1 arg2
- python -r script.py arg1 arg2
- python run script.py --args arg1 arg2
- python script.py arg1 arg2
To run a Python script from the command line and pass arguments, you use the python command followed by the script name and the arguments separated by spaces, like python script.py arg1 arg2. This allows you to pass arguments to your script for processing.
How would you replace all NaN values in a DataFrame with zeros in Pandas?
- df.fillna(0)
- df.NaNToZero()
- df.replace(NaN, 0)
- df.zeroNaN()
To replace all NaN values with zeros in a Pandas DataFrame, you can use the fillna() method with the argument 0. This will fill all NaN occurrences with zeros.
How would you prevent overfitting in a deep learning model when using frameworks like TensorFlow or PyTorch?
- By increasing the model's complexity to better fit the data.
- By reducing the amount of training data to limit the model's capacity.
- By using techniques like dropout, regularization, and early stopping.
- Overfitting cannot be prevented in deep learning models.
To prevent overfitting, you should use techniques like dropout, regularization (e.g., L1, L2), and early stopping. These methods help the model generalize better to unseen data and avoid fitting noise in the training data. Increasing model complexity and reducing training data can exacerbate overfitting.
How would you override a method defined in a superclass in Python?
- By creating a new method with the same name in the subclass
- By importing the superclass method
- By renaming the superclass method
- By using the @override decorator
In Python, to override a method defined in a superclass, you create a new method with the same name in the subclass. This new method in the subclass will replace (override) the behavior of the superclass method.
How would you organize a group of related functions into a module?
- By declaring them in the global scope.
- By defining them inside an object literal.
- By placing them in a separate JavaScript file and exporting them using the export keyword.
- By using classes and inheritance.
To organize a group of related functions into a module, you should place them in a separate JavaScript file and export them using the export keyword. This helps maintain code modularity and reusability.
How would you optimize the space complexity of a dynamic programming algorithm?
- Increase the input size to reduce space complexity
- Optimize time complexity instead
- Use a brute-force approach
- Use memoization to store intermediate results
To optimize space complexity in dynamic programming, you can use memoization (caching) to store intermediate results, avoiding redundant calculations and reducing memory usage.
How would you optimize the performance of a RESTful API that serves large datasets?
- A. Use HTTP GET for all requests
- B. Implement pagination and filtering
- C. Remove all error handling for faster processing
- D. Use a single, monolithic server
B. Implementing pagination and filtering allows clients to request only the data they need, reducing the load on the server and improving performance. Options A, C, and D are not recommended practices and can lead to performance issues.
How would you optimize the performance of a deep learning model in TensorFlow or PyTorch during the inference stage?
- A. Quantization
- B. Data Augmentation
- C. Gradient Clipping
- D. Model Initialization
Option A, Quantization, is a common optimization technique during the inference stage. It involves reducing the precision of model weights and activations, leading to smaller memory usage and faster inference. Option B, Data Augmentation, is typically used during training, not inference. Option C, Gradient Clipping, is a training technique to prevent exploding gradients. Option D, Model Initialization, is essential for training but less relevant during inference.