Imagine you are developing a plugin system where plugins need to register themselves upon definition. How could a metaclass facilitate this registration process?
- Metaclasses can automatically register plugins by scanning the codebase for plugin classes.
- Metaclasses can create a global registry and automatically register plugins when they are defined by modifying the metaclass's __init__ method.
- Metaclasses cannot assist in plugin registration.
- Plugins can self-register by implementing a specific interface, and the metaclass can validate their registration by checking for this interface.
Metaclasses can help manage plugin registration by imposing registration checks and maintaining a central registry for plugins as they are defined.
In a ____, each element points to the next one, forming a sequence.
- Array
- Heap
- Linked List
- Stack
In a "Linked List," each element (node) contains data and a reference (or pointer) to the next node, forming a sequence. Linked lists are versatile data structures used in various applications, including dynamic data storage.
In a ____, each node contains a reference to the next node in the sequence.
- Array
- Linked List
- Queue
- Stack
A Linked List is a data structure in which each node contains a reference to the next node. This makes it a suitable choice for dynamic data structures where elements can be easily added or removed at any position.
In a binary tree, a node with no children is called a _____.
- Branch node
- Leaf node
- Root node
- Traversal
In a binary tree, a node with no children is called a "leaf node." Leaf nodes are the endpoints of the tree and have no child nodes. They are essential in various tree operations and algorithms.
In a Flask application, you are required to implement user authentication. How would you securely manage user passwords?
- Hash and salt user passwords before storage
- Store passwords in plain text for easy retrieval
- Transmit passwords in HTTP headers for convenience
- Use symmetric encryption for password storage
Securely managing user passwords in Flask involves hashing and salting them before storage. This ensures that even if the database is compromised, attackers can't easily recover passwords.
In algorithm analysis, ____ denotes the upper bound of the running time of an algorithm.
- Big-O
- O-notation
- Θ-notation
- Ω-notation
In algorithm analysis, Big-O notation (often represented as O-notation) denotes the upper bound of the running time of an algorithm. It provides an upper limit on how the algorithm's runtime scales with input size.
In deep learning models built using TensorFlow or PyTorch, the ____ method is used to update the model parameters based on the computed gradients.
- backward
- fit
- optimize
- predict
In deep learning frameworks like TensorFlow or PyTorch, the optimize method (or optimizer) is used to update the model parameters (weights) based on the computed gradients during training. This step is a crucial part of the training process as it helps the model learn from the data.
How would you handle collisions in a hash table?
- Ignore the new value
- Replace the existing value with the new one
- Resize the hash table
- Use linear probing
Collisions in a hash table can be handled by using techniques like linear probing, which involves searching for the next available slot in the table when a collision occurs. This ensures that all values are eventually stored without excessive collisions.
How would you handle large DataFrames that do not fit into memory using Pandas?
- Reducing the precision of data
- Reshaping the DataFrame
- Splitting the DataFrame into smaller chunks
- Using the Dask library
When dealing with large DataFrames that do not fit into memory, you can use the Dask library, which allows for distributed computing and can handle larger-than-memory datasets.
How would you handle missing data for a numerical feature in a dataset before training a machine learning model?
- Ignore missing data, it won't affect the model
- Remove the rows with missing data
- Replace missing values with a random value
- Replace missing values with the mean of the feature
Handling missing data is crucial. Replacing missing values with the mean of the feature is a common practice as it retains data and doesn't introduce bias, especially in numerical features. Removing rows or using random values can lead to loss of information or noise.