When optimizing Python code, why is it essential to consider the algorithmic complexity of your solutions?
- Algorithmic complexity only matters for other programming languages, not Python.
- It helps you write faster code by choosing algorithms that have better time complexity.
- It makes your code longer and more complex.
- It's not necessary; you can optimize code without worrying about algorithmic complexity.
Considering algorithmic complexity is crucial because it determines how efficiently your code runs as the input size grows. Choosing algorithms with better time complexity can significantly improve performance.
When preprocessing data, the ____ class in Scikit-learn is used to encode categorical features as a one-hot numeric array.
- LabelEncoder
- OneHotEncoder
- PCA
- StandardScaler
In Scikit-learn, the OneHotEncoder class is used to transform categorical features into a one-hot encoded numeric array. This process is essential when working with machine learning algorithms that require numeric input, as it converts categorical data into a format that the algorithms can understand.
When should you consider using HTTP long-polling in designing RESTful APIs?
- A. When real-time updates are required
- B. For caching static content
- C. To minimize latency in request-response cycles
- D. When working with large file uploads
A. HTTP long-polling is suitable when you need real-time updates from the server. It involves keeping a connection open until new data is available, making it suitable for applications like chat or notifications. Options B, C, and D are not appropriate use cases for long-polling.
When using decorators, what is the significance of the functools.wraps function?
- It is not related to decorators.
- It is used to define custom decorators.
- It is used to wrap a function multiple times.
- It's used to create a wrapper function that preserves metadata like function name and docstring when decorating a function.
The functools.wraps function in Python is often used when creating decorators. It helps preserve the metadata of the original function (e.g., name and docstring) when decorating it with another function. This ensures that the decorated function retains its identity and documentation.
When using Python’s PDB, the command ____ is used to step into a function call.
- next
- step
- step_in
- step_into
In Python's PDB (Python Debugger), the step_into command is used to step into a function call during debugging. It allows you to go into the called function and debug its execution.
When using Scikit-learn, what is the initial step to perform before fitting a model to the dataset?
- Import the required functions and classes
- Install Scikit-learn
- Normalize the data
- Split the data into training and testing sets
The initial step when using Scikit-learn is to import the necessary functions and classes from the library. This allows you to access the machine learning models and tools you need for data preprocessing, model training, and evaluation.
When using TensorFlow or PyTorch, the ____ method is used to load a pre-trained model.
- create_model
- import_model
- initialize_model
- load_model
When working with deep learning frameworks like TensorFlow or PyTorch, you typically use the load_model method to load a pre-trained model from a file. This allows you to reuse a model that has been previously trained on a large dataset.
When using the unittest framework, which method is executed before each test method is run?
- beforeTest()
- init()
- setUp()
- setUpClass()
In the unittest framework, the setUp() method is executed before each test method is run. This method is used to set up any preconditions or resources needed for the test.
Which algorithm would you use to find the shortest path in an unweighted graph?
- Bellman-Ford algorithm
- Breadth-First Search (BFS)
- Depth-First Search (DFS)
- Dijkstra's algorithm
In an unweighted graph, the shortest path can be found using Breadth-First Search (BFS). BFS explores the graph level by level, ensuring that the first time you reach a node is by the shortest path.
Which algorithmic paradigm divides the problem into subproblems and solves each one independently?
- Backtracking
- Divide and Conquer
- Dynamic Programming
- Greedy
The divide and conquer paradigm breaks down a problem into smaller subproblems, solves each subproblem independently, and then combines their solutions to form the solution to the original problem. It's commonly used in algorithms like merge sort and quicksort.