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

2. Which of the following best describes the role of MLOps in the context of NVIDIA technologies for deploying machine learning models in production? (Select two)

A) MLOps helps manage the lifecycle of machine learning models, ensuring efficient collaboration and model governance
B) MLOps replaces the need for data preprocessing during training and deployment
C) MLOps ensures that models trained on GPUs can only run on GPUs during deployment
D) MLOps frameworks support version control and automation, ensuring reproducibility and scalability of ML workflows

3. You are working with a time-series dataset containing network traffic logs. You suspect the presence of anomalies, such as distributed denial-of-service (DDoS) attacks or sudden traffic surges.
Which machine learning approach is best suited for detecting these anomalies?

A) Linear Regression
B) Autoencoder-based Anomaly Detection
C) Naive Bayes Classifier
D) k-Nearest Neighbors (k-NN) for Classification

4. A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

A) Using a single-threaded feature extraction approach to avoid overhead from parallelization.
B) Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.
C) Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.
D) Reducing dataset size by randomly removing data points without considering class balance.

5. You are building an MLOps pipeline for a predictive model that uses tabular data with both categorical and numerical features.
To ensure efficient data processing and optimal model training on an NVIDIA GPU, which of the following data types would be most suitable for a categorical feature representing different product categories?

A) Int32
B) Float64
C) String
D) Int64

Solutions: