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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following is the most appropriate way to perform large-scale data processing in a GPU- accelerated environment using NVIDIA RAPIDS?

A) Use TensorFlow for all data manipulations in a GPU environment.
B) Use NumPy exclusively for processing large datasets on GPUs.
C) Use pandas for all data manipulations and rely on multi-threading for parallel execution.
D) Use Dask on top of RAPIDS for distributed computing across multiple GPUs.

2. You are working on an MLOps pipeline that involves loading a large dataset for training a deep learning model on an NVIDIA GPU. Before training, you need to ensure that the dataset fits within the available GPU memory.
Which of the following commands in Python using the pandas and numpy libraries can correctly determine the memory size of a dataset?

A) df.memory_usage(deep=True).sum()
B) df.info(memory_usage='deep')
C) np.array(df).nbytes
D) sys.getsizeof(df)

3. A machine learning engineer is benchmarking a GPU-accelerated pipeline for data preprocessing and training. The pipeline consists of large-scale data transformations using RAPIDS cuDF and training a model with TensorFlow. The engineer wants to ensure that GPU utilization is maximized throughout the workflow.
Which approach is most effective in achieving this?

A) Pipeline execution should be asynchronous, overlapping data preprocessing with model training.
B) Precompute all features on the CPU before loading them into the GPU for training.
C) Reduce the dataset size to fit entirely in GPU memory, even if it leads to data loss.
D) Use a single GPU to avoid overhead associated with multi-GPU synchronization.

4. You are analyzing a transportation network where airports represent nodes and flight routes represent edges. You need to determine the most critical airports in the network based on how many shortest paths pass through them.
Which cuGraph centrality algorithm should you use for this task?

A) Betweenness Centrality, as it identifies airports that act as major transit hubs by measuring how frequently they appear in shortest paths.
B) Triangle Count, as it determines the number of three-node cycles an airport is part of, indicating its importance in network structure.
C) Closeness Centrality, as it measures how close an airport is to all other airports, making it the best indicator of global connectivity.
D) Degree Centrality, as it measures the number of direct connections an airport has, indicating its importance in the network.

5. You are training a convolutional neural network (CNN) model with a large dataset on a single GPU.
The model is consuming too much GPU memory, and training is slow.
Which of the following techniques would help you reduce GPU memory consumption while maintaining or improving the efficiency of training? (Select two)

A) Implement mixed precision training to lower memory usage and speed up computation without losing accuracy.
B) Use a smaller batch size to reduce the memory requirements per training iteration, allowing for faster training.
C) Use batch normalization to reduce memory usage by skipping some of the computations during training.
D) Use automatic differentiation for every operation to save memory during the backpropagation phase.
E) Decrease the size of the model by reducing the number of layers or the number of filters per layer.

Solutions: