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

2. A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.
Which NVIDIA-supported tool is best suited for this purpose?

A) Precompute the time-series visualization with Dask and display it in a static HTML page.
B) Use Plotly Dash with RAPIDS cuDF to create an interactive GPU-powered dashboard.
C) Convert the stock price dataset into a NumPy array and visualize it using Seaborn's line plot.
D) Rely on Matplotlib to generate static plots and update them every minute with a loop.

3. You are processing a large dataset in a distributed computing environment using RAPIDS and Dask.
Your workflow involves frequent shuffling of data between partitions, leading to significant slowdowns.
Which of the following strategies is the best way to implement data caching to reduce shuffle overhead using NVIDIA technologies?

A) Use traditional disk-based caching by writing intermediate results to CSV files and reloading when needed.
B) Use a CPU-based caching solution like Memcached to store intermediate data before reloading into cuDF.
C) Disable caching altogether to force a recomputation of results, ensuring up-to-date data processing.
D) Enable GPU-accelerated caching with RAPIDS cuDF and persist intermediate results in GPU memory.

4. A data scientist is working with large-scale tabular datasets and wants to optimize data ingestion and storage for accelerated processing on NVIDIA GPUs. The scientist is considering different file formats and storage optimizations to maximize performance in a RAPIDS-based workflow.
Which of the following approaches is the most suitable for optimizing both storage and processing performance?

A) Store data in Parquet format and load it using cuDF in RAPIDS.
B) Convert datasets into CSV format and store them in local disk storage.
C) Use JSON format for easy readability and process it directly in cuDF.
D) Load data directly into NumPy arrays before using RAPIDS cuDF for processing.

5. You are a data scientist analyzing a social media network with NVIDIA cuGraph to identify the most influential users using the PageRank algorithm.
Which option best describes how cuGraph PageRank operates on a directed graph?

A) PageRank in cuGraph uses an iterative power method to update node importance values based on incoming edges, incorporating a damping factor to handle random jumps.
B) PageRank in cuGraph is a label propagation algorithm that clusters nodes into communities rather than ranking their importance.
C) PageRank in cuGraph operates only on undirected graphs and cannot be applied to networks where edges have a direction.
D) PageRank assigns equal importance to all nodes in the graph initially and updates values only based on outgoing edges, ignoring incoming edges.

Solutions: