How would you replace all NaN values in a DataFrame with zeros in Pandas?

  • df.fillna(0)
  • df.NaNToZero()
  • df.replace(NaN, 0)
  • df.zeroNaN()
To replace all NaN values with zeros in a Pandas DataFrame, you can use the fillna() method with the argument 0. This will fill all NaN occurrences with zeros.

How would you prevent overfitting in a deep learning model when using frameworks like TensorFlow or PyTorch?

  • By increasing the model's complexity to better fit the data.
  • By reducing the amount of training data to limit the model's capacity.
  • By using techniques like dropout, regularization, and early stopping.
  • Overfitting cannot be prevented in deep learning models.
To prevent overfitting, you should use techniques like dropout, regularization (e.g., L1, L2), and early stopping. These methods help the model generalize better to unseen data and avoid fitting noise in the training data. Increasing model complexity and reducing training data can exacerbate overfitting.

How would you override a method defined in a superclass in Python?

  • By creating a new method with the same name in the subclass
  • By importing the superclass method
  • By renaming the superclass method
  • By using the @override decorator
In Python, to override a method defined in a superclass, you create a new method with the same name in the subclass. This new method in the subclass will replace (override) the behavior of the superclass method.

How would you organize a group of related functions into a module?

  • By declaring them in the global scope.
  • By defining them inside an object literal.
  • By placing them in a separate JavaScript file and exporting them using the export keyword.
  • By using classes and inheritance.
To organize a group of related functions into a module, you should place them in a separate JavaScript file and export them using the export keyword. This helps maintain code modularity and reusability.

How would you optimize the space complexity of a dynamic programming algorithm?

  • Increase the input size to reduce space complexity
  • Optimize time complexity instead
  • Use a brute-force approach
  • Use memoization to store intermediate results
To optimize space complexity in dynamic programming, you can use memoization (caching) to store intermediate results, avoiding redundant calculations and reducing memory usage.

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 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.

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 a Python function that is found to be CPU-bound during profiling?

  • a) Use a Just-In-Time (JIT) compiler like PyPy.
  • b) Increase the number of threads to parallelize the code.
  • c) Optimize the algorithm or use data structures that are more efficient.
  • d) Use a faster computer for running the code.
When a Python function is CPU-bound, the most effective optimization is usually to optimize the algorithm or use more efficient data structures. JIT compilation (a) can help in some cases, but it may not be as effective as algorithmic improvements. Increasing the number of threads (b) might help if the code can be parallelized, but this is not always the case. Using a faster computer (d) is generally not a solution to CPU-bound code as it doesn't address the underlying inefficiencies.

How would you investigate memory leaks in a Python application?

  • Manually inspect each variable in the code to find memory leaks.
  • Use a memory profiler like memory_profiler to track memory usage over time.
  • Use the psutil library to monitor CPU usage and infer memory leaks.
  • Use the timeit module to measure execution time and find memory leaks.
To investigate memory leaks in a Python application, you can use a memory profiler like memory_profiler, which tracks memory usage over time, helping you identify areas of concern. Manual inspection (Option 3) is impractical for large codebases, and psutil (Option 2) primarily focuses on CPU usage. The timeit module (Option 4) measures execution time, not memory usage.

How would you initialize an empty list in Python?

  • empty_list = []
  • empty_list = [None]
  • empty_list = {}
  • empty_list = None
To initialize an empty list in Python, you use square brackets []. Option 2 initializes an empty dictionary, option 3 initializes a variable as None, and option 4 initializes a list with a single element, which is None.

How would you implement rate limiting in a RESTful API to prevent abuse?

  • A. Use JWT tokens
  • B. Implement a token bucket algorithm
  • C. Limit requests based on IP addresses
  • D. Disable API access during peak hours
B. Implementing a token bucket algorithm is a common method for rate limiting in RESTful APIs. It allows you to control the rate of requests over time, preventing abuse while allowing legitimate usage. Options A, C, and D are not effective methods for rate limiting.