NVIDIA : NCP-ADS Questions & Answers

You are working with a dataset containing billions of records stored in a Parquet file. You need to load this dataset efficiently into an NVIDIA-accelerated RAPIDS environment for feature engineering.
Which of the following is the best approach?

• A. Load the Parquet file into Dask and then convert it into a cuDF DataFrame for parallel processing
• B. Convert the dataset into a CSV format and use cudf.read_csv() to load it into RAPIDS
• C. Use pandas.read_parquet() to load the dataset and then convert it to a cuDF DataFrame
• D. Load the Parquet file directly into a cuDF DataFrame using cudf.read_parquet()

You are optimizing a data pipeline for a large-scale machine learning project using NVIDIA RAPIDS and Apache Spark. The pipeline performs many expensive shuffle operations.
Which of the following is the most effective method to reduce shuffle and improve performance using NVIDIA technologies?

• A. Implement a custom shuffle partitioning scheme using NVIDIA DALI for more control over data partitioning during shuffle operations.
• B. Store data in HDFS before performing shuffle operations to reduce GPU memory overhead.
• C. Use RAPIDS cuDF to repartition the data in the GPU memory after each shuffle.
• D. Use RAPIDS cuDF to perform in-memory data processing on GPU before shuffling to avoid network communication.

A financial institution is using cuGraph to analyze transaction data and detect potential fraudulent activity. The institution wants to identify users who have a high likelihood of being involved in suspicious activities based on the structure of their transactions.
Which of the following cuGraph algorithms would be the best choice for this task?

• A. Breadth-First Search (BFS)
• B. Spectral Clustering
• C. Weakly Connected Components
• D. Betweenness Centrality

A machine learning engineer is training a convolutional neural network (CNN) on an NVIDIA GPU and needs to maximize throughput while avoiding OOM errors.
Which of the following techniques is the most effective way to balance memory efficiency and training speed?

• A. Loading all dataset samples into GPU memory at the start of training
• B. Allocating a fixed batch size without monitoring memory usage
• C. Using dynamic batch sizing based on available GPU memory
• D. Using a batch size of 1 to minimize memory usage

Which of the following best describes the purpose of the NVIDIA TensorRT library?

• A. Accelerates training of neural networks
• B. Provides hardware abstraction for AI model development
• C. Manages GPU resources for deep learning models
• D. Optimizes and accelerates inference of trained models