Prep and try our NCP-ADS valid and latest training questions & answers

You are training a machine learning model using scikit-learn-like API on a dataset with millions of samples and thousands of features. You need to optimize both training time and inference speed using NVIDIA technologies.
Which solution is the most appropriate?

• A. Use NVIDIA Triton Inference Server to train the model efficiently on a single GPU.
• B. Use NVIDIA Modulus to accelerate machine learning training and feature selection.
• C. Use NVIDIA RAPIDS cuML for GPU-accelerated machine learning model training.
• D. Use NVIDIA Magnum IO to optimize machine learning model parameters on the GPU.

You are analyzing a dataset that contains missing values.
Which of the following techniques is most appropriate when dealing with missing numerical data in a dataset, ensuring minimal impact on model performance?

• A. Replacing missing values with the mean of the column
• B. Using k-nearest neighbors (KNN) imputation
• C. Removing rows with missing values
• D. Replacing missing values with a constant value (e.g., zero)

Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

• A. Min-Max Scaling
• B. Log Transformation
• C. Robust Scaling
• D. Z-score Standardization

A machine learning engineer is working on a multi-GPU workload using Dask and RAPIDS to process a massive dataset efficiently. However, they notice that GPU utilization is not optimal, and data transfer between GPUs is slowing down computation.
What is the best approach to minimize data transfer overhead and maximize parallel efficiency?

• A. Load the dataset into a Pandas DataFrame and distribute computations across GPUs manually
• B. Use Dask's single-threaded scheduler to avoid multi-threading overhead
• C. Transfer data between GPUs using NumPy's .copy() function
• D. Use NVLink to enable high-bandwidth inter-GPU communication and dask_cuda.LocalCUDACluster

You are analyzing a large-scale transportation network using cuGraph and notice that query times are longer than expected when running graph algorithms.
What is the best way to optimize graph processing performance using GPU-accelerated tools?

• A. Convert the graph to CSR (Compressed Sparse Row) format before running computations to improve memory efficiency.
• B. Use cugraph.to_directed() to convert the graph into a directed format, which improves GPU parallelism.
• C. Store the graph in COO (Coordinate List) format instead of CSR (Compressed Sparse Row) format for faster traversal.
• D. Use cugraph.filter_unconnected_nodes() to remove unconnected nodes before processing.