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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q46-Q51):

NEW QUESTION # 46
You are working with a large dataset containing millions of high-resolution images for a deep learning project. The dataset needs to be processed efficiently on a GPU before training a model.
Which NVIDIA technology is best suited for preprocessing, augmenting, and efficiently loading the dataset into memory?

Answer: B


NEW QUESTION # 47
You are building a deep learning model using TensorFlow with cuDNN acceleration on an NVIDIA GPU. Your dataset contains continuous numerical features with vastly different ranges.
What is the best way to standardize the data efficiently to improve model convergence?

Answer: B


NEW QUESTION # 48
You are training a deep learning model on a large dataset. Initially, you train the model on a single GPU and achieve a training time of 10 hours. To speed up training, you switch to a multi-GPU setup with four GPUs. However, after testing, you notice that the training time is only reduced to 3.5 hours instead of the expected 2.5 hours (a linear speedup).
What is the most likely reason for this sublinear speedup?

Answer: B


NEW QUESTION # 49
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?

Answer: B


NEW QUESTION # 50
You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?

Answer: A


NEW QUESTION # 51
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