NVIDIA-Certified-Professional Accelerated Data Science - NCP-ADS certification

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

You are working on an MLOps project where GPU-accelerated workflows are being used for model training. You want to benchmark and optimize these workflows to ensure the best performance.
Which of the following steps should you consider to effectively benchmark and optimize GPU- accelerated workflows? (Select two)

A. Increase the batch size and learning rate simultaneously to maximize GPU usage and reduce training time.
B. Optimize data loading by using data augmentation techniques during training to reduce the time spent on I/O operations.
C. Use profiling tools to measure the GPU utilization and memory usage during training to identify performance bottlenecks.
D. Use a dynamic batch size strategy that adjusts the batch size based on available GPU memory to maximize throughput.

Question 2

You are building a predictive model for retail sales forecasting and need a dataset that includes historical sales transactions, customer demographics, and external economic indicators (e.g., inflation rate, unemployment rate).
Which of the following datasets would be the most appropriate for your model?

A. A dataset of product reviews and customer sentiments from an e-commerce website
B. A dataset containing transaction history and customer profiles from a retail company
C. A dataset with global temperature trends over the past decade
D. A public dataset of annual GDP per country

Question 3

You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?

A. RAPIDS + Dask for distributed GPU-accelerated ETL
B. Apache Spark with its default CPU-based execution
C. Hadoop MapReduce
D. Pandas with multiprocessing

Question 4

You are training a deep learning model for image classification and want to optimize its hyperparameters, including learning rate, batch size, and number of layers.
Which of the following techniques is the most effective for efficiently searching through a high- dimensional hyperparameter space?

A. Gradient Descent
B. Grid Search
C. Random Search
D. Bayesian Optimization

Question 5

You need to deploy a machine learning model on a GPU-equipped system. The GPU has 16GB of VRAM, and the model requires approximately 12GB of memory during inference. However, additional system processes and other applications consume 5GB of VRAM.
What would happen if you attempt to run inference without making any optimizations, and how should you resolve the issue?

A. The model will run without issues because 16GB of VRAM is sufficient for a 12GB model
B. The model will fail to run due to out-of-memory (OOM) errors, and using a smaller batch size can help reduce memory usage
C. The model will run successfully but with reduced performance due to memory fragmentation
D. Switching from a GPU to CPU inference will resolve memory issues without performance loss

Solutions: