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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q50-Q55):

NEW QUESTION # 50
You are processing large-scale datasets in Dask-cuDF and observe that your computation involves excessive data shuffling, which slows down performance. You decide to implement data caching to reduce shuffle overhead.
Which of the following best describes a technique to reduce shuffle costs in a Dask-cuDF workflow?

Answer: B


NEW QUESTION # 51
You are processing a dataset of high-resolution images for deep learning training and want to optimize image loading, augmentation, and transformation on an NVIDIA GPU.
Which of the following statements correctly describes the role of NVIDIA DALI (Data Loading Library) in accelerating data preparation?

Answer: A


NEW QUESTION # 52
You are working with a dataset containing billions of rows and need to perform data transformations, aggregations, and joins efficiently on a single-node GPU-enabled workstation.
Which NVIDIA technology is best suited to optimize performance for these operations?

Answer: D


NEW QUESTION # 53
You are tasked with profiling a PyTorch-based deep learning model to identify performance bottlenecks using NVIDIA DLProf. Your goal is to analyze kernel execution times and identify operations causing excessive memory consumption.
Which of the following steps is the MOST appropriate sequence for profiling using DLProf?

Answer: A


NEW QUESTION # 54
You have a pandas DataFrame with a column containing floating-point numbers, but it takes up too much memory. You want to convert it into a lower-precision type using CuDF or pandas while ensuring computational efficiency.
Which function would you use?

Answer: C


NEW QUESTION # 55
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