NVIDIA NCP-ADS : NVIDIA-Certified-Professional Accelerated Data Science

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are setting up a GPU-accelerated data science environment on a cloud-based instance that utilizes NVIDIA GPUs.
To ensure compatibility between CUDA, RAPIDS, and Python libraries, which of the following is the most effective approach for managing dependencies and avoiding version conflicts?

A) Manually install required libraries in a system-wide Python environment using pip install without version specifications
B) Use a virtual machine with all dependencies pre-installed to avoid any software conflicts
C) Use Conda environments with version-pinned dependencies to create an isolated environment for RAPIDS and CUDA
D) Install RAPIDS, PyTorch, TensorFlow, and other libraries in the same Conda base environment for simplified access

2. You are working with a large-scale social network dataset and need to analyze relationships between users to detect communities using the Louvain algorithm. Given the benefits of GPU acceleration, you decide to use cuGraph for this task.
Which of the following statements best describes why cuGraph is beneficial for this workload?

A) cuGraph does not support weighted graphs, making it less useful for real-world social network analysis.
B) cuGraph primarily accelerates deep learning tasks and is not optimized for graph-based algorithms.
C) Running graph algorithms on a GPU leads to higher latency due to the overhead of data movement between CPU and GPU.
D) cuGraph enables parallel computation on the GPU, significantly speeding up community detection tasks compared to traditional CPU-based implementations.

3. Which of the following is the most efficient method for processing big data in a distributed environment using NVIDIA technologies?

A) Using a single-node GPU-based solution for data processing.
B) Relying on traditional CPU-only processing frameworks for big data tasks.
C) Using cloud-based, non-GPU infrastructure for data processing
D) Utilizing a distributed data processing framework like Apache Spark with GPU acceleration from the NVIDIA RAPIDS library.

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

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

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

Solutions: