Free NVIDIA NCP-ADS Exam Dumps Questions & Answers

A data scientist is working with datasets ranging from hundreds of megabytes to several terabytes and needs to select the most efficient NVIDIA-accelerated data processing library for optimal memory management and performance.
Which approach is best for selecting the appropriate library for different dataset sizes?

• A.  Use Pandas for all dataset sizes since Pandas has built-in multi-threading optimizations.
• B.  Use RAPIDS cuML for handling all data processing tasks, regardless of dataset size.
• C.  Use cuDF for small datasets and Dask-cuDF for larger datasets that do not fit in a single GPU's memory.
• D.  Always use Dask regardless of dataset size since Dask automatically scales from small to large datasets.

You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?

• A.  Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
• B.  Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs
• C.  Use Dask DataFrame to parallelize deep learning model training across multiple GPUs
• D.  Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution

A data scientist is analyzing large-scale sensor readings from an industrial IoT system and wants to visualize high-frequency time-series data efficiently.
Which approach using NVIDIA technologies would be the most effective for interactive visualization of this dataset?

• A.  Use NVIDIA OptiX to ray-trace the time-series dataset for visualization.
• B.  Use RAPIDS cuDF with Datashader to render large time-series data on the GPU efficiently.
• C.  Apply TensorRT to compress the time-series data and use PyTorch's native visualization functions.
• D.  Process the data using RAPIDS cuDF, then convert it to CSV and visualize it with Excel charts.

A data scientist is working with a large dataset containing millions of records and aims to accelerate the data preprocessing workflow using NVIDIA technologies.
Which of the following approaches is the most effective for optimizing data preprocessing performance using GPUs?

• A.  Using NumPy for array computations and parallelizing with Python's multiprocessing
• B.  Running data transformations in a multi-threaded CPU environment
• C.  Using Dask to distribute processing across multiple CPU cores
• D.  Using RAPIDS cuDF to replace pandas operations

You are comparing the performance of GPU-accelerated deep learning models on two cloud platforms: AWS EC2 and Google Cloud Platform (GCP). You want to design a benchmark that evaluates GPU resource utilization, processing time, and cost-efficiency for training models with large datasets.
Which actions should you take to implement an effective benchmark? (Select two)

• A.  Limit the benchmarking tests to a single model architecture for each cloud platform.
• B.  Run each model on the same GPU type and instance size across both cloud platforms.
• C.  Measure both GPU utilization and network latency between the cloud platform and your data storage location.
• D.  Only consider the cost of GPU resources for each cloud provider without factoring in data storage and transfer costs.