Free NVIDIA NCP-ADS Exam Dumps Questions & Answers

You are working on a large-scale machine learning project that requires preprocessing terabytes of structured and semi-structured data. You need a distributed data processing framework that can leverage NVIDIA GPUs efficiently to accelerate computations.
Which of the following approaches would best achieve this goal?

• A.Using TensorFlow's Dataset API to load and preprocess the data on GPUs
• B.Using plain NumPy with CUDA extensions to manually parallelize computations across multiple GPUs
• C.Using Apache Spark with PySpark for CPU-based distributed data processing
• D.Using Dask with RAPIDS cuDF and cuML for distributed GPU-accelerated processing

Which of the following best describes the role of MLOps in the context of NVIDIA technologies for deploying machine learning models in production? (Select two)

• A.MLOps frameworks support version control and automation, ensuring reproducibility and scalability of ML workflows
• B.MLOps replaces the need for data preprocessing during training and deployment
• C.MLOps helps manage the lifecycle of machine learning models, ensuring efficient collaboration and model governance
• D.MLOps ensures that models trained on GPUs can only run on GPUs during deployment

A data scientist is working with large-scale datasets in a RAPIDS AI pipeline and needs to efficiently process and organize the data while leveraging GPU acceleration.
Which of the following approaches best ensures optimized processing and memory management when using NVIDIA technologies?

• A.Store data in a pandas DataFrame first and then convert it to cuDF only when GPU operations are needed.
• B.Write intermediate data to disk as CSV files before reading them back into RAPIDS AI to avoid memory overflow.
• C.Process data using Apache Spark on CPU before transferring results to GPU for model training.
• D.Use cuDF to store and process data in GPU memory, leveraging its vectorized operations for transformations and aggregations.

Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?

• A.Number of model parameters.
• B.Hyperparameter tuning results.
• C.GPU utilization and memory usage.
• D.Training dataset bias.

You are training a deep learning model on a large dataset. Initially, you train the model on a single GPU and achieve a training time of 10 hours. To speed up training, you switch to a multi-GPU setup with four GPUs. However, after testing, you notice that the training time is only reduced to 3.5 hours instead of the expected 2.5 hours (a linear speedup).
What is the most likely reason for this sublinear speedup?

• A.The optimizer is not designed for multi-GPU training, causing inefficiency.
• B.Increased memory bandwidth usage causes a bottleneck in the system.
• C.Data transfer and communication overhead between GPUs limit scalability.
• D.The learning rate is too low, causing slower convergence despite multiple GPUs.