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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is working on a customer segmentation model using NVIDIA RAPIDS on GPUs. The dataset contains millions of customer records with features such as transaction history, age, location, and frequency of visits.
To optimize feature engineering using NVIDIA technologies, what is the best approach?

A) Convert all categorical variables into string representations to preserve their original format for later analysis.
B) Store all numerical features in float64 format to prevent rounding errors during transformations.
C) Perform feature engineering on CPUs using pandas before transferring data to the GPU for training.
D) Use cuDF to perform feature transformations like normalization and one-hot encoding directly on the GPU.

2. 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) Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence
D) Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations

3. You are working on an AI-driven customer behavior prediction project.
According to the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, what is the most critical task to complete during the Data Understanding phase?

A) Deploying the model into a production environment for real-time inference.
B) Selecting the most appropriate machine learning algorithm for the prediction task.
C) Identifying and preparing feature engineering strategies to improve model accuracy.
D) Acquiring and exploring the dataset to assess quality, completeness, and potential biases.

4. Which of the following tools in the NVIDIA AI stack is specifically designed to accelerate the deployment of machine learning models for production by optimizing inference performance?

A) cuBLAS
B) cuML
C) DLA (Deep Learning Accelerator)
D) Triton Inference Server

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

Solutions: