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 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 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 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 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 use a metaclass to automatically register all subclasses of a base class in Python?
- Define a register_subclasses function within the base class.
- Subclasses cannot be automatically registered using metaclasses.
- Use the @register_subclass decorator in conjunction with a metaclass.
- You can use the __init_subclass__ method in a metaclass to automatically register subclasses.
The __init_subclass__ method in a metaclass allows you to automatically register subclasses when they are defined, enabling a way to track and manage them.
If multiple base classes have methods with the same name, method resolution in a derived class follows the _______ rule.
- FIFO (First In, First Out)
- LIFO (Last In, First Out)
- LOO (Last Out, Out)
- MRO (Method Resolution Order)
In Python, when multiple base classes have methods with the same name, the method resolution follows the Method Resolution Order (MRO) to determine which method to call.
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.
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 implement a dequeue (double-ended queue) data structure?
- Array
- Linked List
- Queue
- Stack
A dequeue can be efficiently implemented using a doubly linked list, where you can add or remove elements from both ends in constant time. While arrays are also used, they may not provide the same level of efficiency for both ends' operations.