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 ensure that a piece of code in a module is only executed when the module is run as a standalone program and not when it is imported?
- #execute_if_standalone
- #only_run_when_main
- #standalone_code
- if name == "main":
To ensure that a piece of code in a Python module is only executed when the module is run as a standalone program and not when it is imported, you can use the special if __name__ == "__main__": conditional statement. Code inside this block will only run when the module is the main entry point of the program.
How would you find the loop in a linked list?
- Iterate through the list and check for a null reference
- Use a hash table to store visited nodes
- Use a stack to track visited nodes
- Use Floyd's Tortoise and Hare algorithm
Floyd's Tortoise and Hare algorithm is a popular technique to detect a loop in a linked list. It involves two pointers moving at different speeds through the list. If there's a loop, they will eventually meet. The other options are not efficient for loop detection.
How would you find the shortest path in a weighted graph?
- A* Algorithm
- Breadth-First Search
- Depth-First Search
- Dijkstra's Algorithm
Dijkstra's Algorithm is used to find the shortest path in a weighted graph with non-negative edge weights. It guarantees the shortest path but doesn't work with negative weights. Breadth-First and Depth-First Search are used for different purposes, and A* is for finding the shortest path with heuristics.
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.