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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are deploying a deep learning model on an edge device with 8GB of available RAM. The model's estimated peak memory usage, including model weights, intermediate tensors, and batch data, is 9.5GB.
What is the best course of action to ensure successful deployment while maintaining performance?

A) Reduce the number of model parameters by removing layers from the architecture
B) Offload some computation to cloud-based processing
C) Reduce the batch size during inference
D) Increase the device's swap space to compensate for insufficient RAM

2. When performing benchmarking and optimization for GPU-accelerated workflows, which of the following tools is best suited for analyzing the memory utilization and computational efficiency of deep learning models running on Nvidia GPUs?

A) Nvidia CUDA Profiler
B) Nvidia Nsight Compute
C) Nvidia Riva
D) Nvidia TensorRT

3. You are working on a large dataset for a machine learning model that will be trained using RAPIDS cuML. The dataset includes categorical, integer, and floating-point features.
Which of the following approaches is the best practice for determining the optimal data type choice for each feature using NVIDIA's RAPIDS cuDF library?

A) Use float16 for all floating-point data to reduce memory usage and increase GPU processing speed.
B) Convert categorical variables into int8 to optimize GPU memory usage.
C) Convert all numerical data to float64 for maximum precision in calculations.
D) Use float32 instead of float64 for floating-point numbers when possible, and leverage int8, int16, or int32 for categorical and integer data based on their range.

4. A data scientist is analyzing a large time-series dataset containing stock price movements of thousands of companies over a decade. The dataset is stored as a cuDF DataFrame and contains millions of rows. The scientist wants to visualize trends and patterns interactively while leveraging GPU acceleration.
Which of the following approaches is the most efficient for visualizing this time-series data?

A) Use cuXfilter with a cuDF DataFrame to generate interactive visualizations directly on the GPU.
B) Use seaborn with a sampled subset of the dataset to generate line plots.
C) Convert the cuDF DataFrame to Pandas and use matplotlib for plotting.
D) Use matplotlib with plt.plot() while applying df.to_pandas() to convert data.

5. You are processing a large-scale transportation network graph using NVIDIA cuGraph. The graph is extremely large, consuming almost all available GPU memory. Performance is deteriorating, and some computations fail due to memory exhaustion.
What is the best approach to efficiently handle this large graph while keeping computations on the GPU?

A) Use cuGraph's multi-GPU support via Dask-cuGraph to distribute the graph across multiple GPUs.
B) Convert the graph into a NetworkX graph and process it on the CPU to reduce GPU memory usage.
C) Manually split the graph into chunks and process each chunk separately without any coordination.
D) Store the graph as a large Python dictionary and use cuGraph only for specific queries.

Solutions: