NVIDIA NCP-ADS Exam : 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. Which of the following scenarios are most appropriate for using GPU acceleration when working with large-scale datasets in machine learning? (Select two)

A) Running a deep learning model for image classification with millions of labeled images.
B) Running a large ensemble of simple models (e.g., random forests) on a dataset of 10 million rows.
C) Using decision trees for a classification task with a dataset of 1 million rows and 20 features.
D) Training a large-scale natural language processing (NLP) model on text data with billions of words.
E) Performing exploratory data analysis (EDA) on a dataset of 100,000 rows with 10 features.

2. In the CRISP-DM process, which NVIDIA technology is commonly used during the "Data Preparation" phase to handle large datasets efficiently?

A) NVIDIA Deep Learning AI
B) NVIDIA CUDA Toolkit
C) NVIDIA RAPIDS
D) NVIDIA Jetson

3. You have a massive time-series dataset containing millions of records per day, and you need to perform forecasting at scale.
Which of the following techniques best utilizes NVIDIA technologies to optimize time-series forecasting?

A) Use RAPIDS cuDF and Dask-cuDF to preprocess the data and train an LSTM model with PyTorch Lightning on multiple GPUs.
B) Use RAPIDS cuML's XGBoost-GPU implementation to forecast time-series patterns with improved training efficiency.
C) Run Facebook Prophet on a multi-core CPU environment to leverage parallel forecasting.
D) Convert time-series data to tensors and train a deep learning model using PyTorch with mixed precision on an NVIDIA GPU.

4. You are using cuGraph to run the PageRank algorithm on a directed web graph. The dataset is large, and you want to ensure an accurate and efficient computation while optimizing GPU performance.
Which of the following configurations is the best approach for running PageRank in cuGraph?

A) Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold
B) Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format
C) Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations
D) Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence

5. You are working on a large-scale social network analysis project using cuGraph. Your goal is to identify the most influential individuals in the network based on their connectivity and reach.
Which of the following cuGraph algorithms would be the most appropriate choice for this task?

A) Connected Components
B) Single Source Shortest Path (SSSP)
C) Louvain Clustering
D) PageRank

Solutions: