Which library would you primarily use for implementing linear regression in Python?
- Matplotlib
- NumPy
- Pandas
- Scikit-learn
Scikit-learn is a powerful library in Python for machine learning, including linear regression. While NumPy, Pandas, and Matplotlib are essential for data manipulation and visualization, Scikit-learn provides tools for regression tasks, making it the primary choice for linear regression.
Which data structure would be the most appropriate to implement a priority queue?
- Array
- Binary Heap
- Hash Table
- Linked List
A binary heap is the most appropriate data structure to implement a priority queue because it allows efficient insertion and extraction of elements with logarithmic time complexity.
Which command is used in PDB (Python Debugger) to continue execution until the next breakpoint is encountered?
- c
- continue
- go
- next
In the Python Debugger (PDB), you can use the continue command to resume execution of your code until the next breakpoint is encountered. This allows you to step through your code and examine its behavior.
Which data structure follows the Last In First Out (LIFO) principle?
- Array
- Linked List
- Queue
- Stack
A stack is a data structure that follows the Last In First Out (LIFO) principle. It means that the last element added to the stack will be the first one to be removed. Stacks are commonly used in programming for tasks like tracking function calls and managing memory.
Which data structure in Python would you use to store a collection of unique elements?
- Dictionary
- List
- Set
- Tuple
You would use a Set in Python to store a collection of unique elements. Sets do not allow duplicate values, and they are commonly used to work with unique items. Lists (option 1) and Tuples (option 2) allow duplicate elements, and Dictionaries (option 4) store key-value pairs.
When designing a RESTful API, how should you handle versioning of the API?
- Do not version the API
- Hardcode the version in the API endpoints
- Include the version in the URL (e.g., /v1/resource)
- Use a header like "X-API-Version"
The recommended way to handle API versioning in RESTful APIs is by including the version in the URL. This ensures backward compatibility and makes it clear which version of the API a client is using. The other options are not considered best practices.
When implementing a recursive algorithm, what is crucial to avoid infinite recursion?
- A base case
- A high stack size
- A loop
- Global variables
In a recursive algorithm, it's crucial to have a base case that defines when the recursion should stop. Without a base case, the algorithm will keep calling itself indefinitely, leading to infinite recursion and a stack overflow error.
When integrating a Python back-end with a front-end form, how can you secure the application against Cross-Site Request Forgery (CSRF) attacks?
- Disable JavaScript to prevent malicious form submissions.
- Use a unique token with each form submission and verify it on the server.
- Use HTTPS to encrypt form data.
- Validate user input on the front-end before submission.
To secure an application against CSRF attacks, you should use a unique token (CSRF token) with each form submission. This token is generated on the server and verified on the server to ensure that the request is legitimate and not forged by a malicious attacker.
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.