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 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.
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 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.
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.
In Matplotlib, the ____ method is used to create a new figure object.
- create_figure
- figure
- new_figure
- plot
In Matplotlib, the figure method is used to create a new figure object. A figure object is like a canvas where you can add multiple subplots or axes to create complex plots with multiple elements. It is an essential step when working with Matplotlib.
In Matplotlib, the ____ method is used to set the labels of the x-axis.
- set_x_axis
- set_x_label
- set_xlabel
- x_labels
In Matplotlib, you use the set_xlabel method to set the label for the x-axis. This method allows you to specify the label that appears below the x-axis in your plot.
In NumPy, the ____ function is used to calculate the element-wise maximum of two arrays.
- max
- maximize
- maximum
- min
In NumPy, the maximum function is used to calculate the element-wise maximum of two arrays. It returns a new array containing the element-wise maximum values from the input arrays.
In NumPy, the ____ function is used to compute the inverse of a matrix.
- np.inverse()
- np.invert()
- np.linalg.inv()
- np.transpose()
In NumPy, the np.linalg.inv() function is used to compute the inverse of a matrix. This function is essential for various linear algebra operations in NumPy, such as solving linear equations.
In object-oriented programming in Python, ____ refers to the class that a class inherits from.
- base class
- parent class
- subclass
- superclass
In Python, the term "base class" refers to the class that a class inherits from. It's also commonly called a "parent class" or "superclass."