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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a large-scale graph analysis problem that involves computing the shortest paths between nodes in a massive social network dataset. You decide to leverage NVIDIA RAPIDS cuGraph for accelerated computation.
Which of the following cuGraph functions should you use?

A) cugraph.label_propagation()
B) cugraph.k_truss()
C) cugraph.pagerank()
D) cugraph.sssp()

2. You are tasked with optimizing the performance of a large-scale data science project that involves deep learning models on a cloud infrastructure. Your organization is using GPUs for model training.
Which of the following strategies would be the most effective in optimizing GPU performance for data science tasks? (Select two)

A) Utilize multi-GPU training to parallelize the workload, reducing training time.
B) Optimize GPU performance by limiting the number of threads running on each GPU.
C) Overclock the GPU to achieve higher computational speeds and improve training times.
D) Use a single cloud instance with the largest GPU available to ensure maximum performance.
E) Use larger batch sizes to make better use of GPU memory during model training.

3. You are working on a data science project that involves processing large-scale financial transaction data. You want to optimize data manipulation operations using NVIDIA's RAPIDS cuDF.
Which of the following approaches best leverages NVIDIA technologies for efficient data manipulation?

A) Convert the dataset into a SQLite database and execute SQL queries to perform data transformations.
B) Use cuDF DataFrames for data manipulation and rely on GPU-accelerated functions like .groupby(),
.merge(), and .applymap().
C) Load the dataset into a Pandas DataFrame and use multi-threading to speed up operations.
D) Preprocess the data using Apache Spark's CPU-based DataFrame API before transferring it to a GPU for machine learning.

4. You are training a machine learning model using scikit-learn-like API on a dataset with millions of samples and thousands of features. You need to optimize both training time and inference speed using NVIDIA technologies.
Which solution is the most appropriate?

A) Use NVIDIA Modulus to accelerate machine learning training and feature selection.
B) Use NVIDIA RAPIDS cuML for GPU-accelerated machine learning model training.
C) Use NVIDIA Magnum IO to optimize machine learning model parameters on the GPU.
D) Use NVIDIA Triton Inference Server to train the model efficiently on a single GPU.

5. A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?

A) Manually installing each package and its dependencies using pip
B) Using only virtual environments (venv) without managing GPU dependencies separately
C) Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies
D) Using a single system-wide installation of CUDA and forcing all projects to use the same version

Solutions: