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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q290-Q295):

NEW QUESTION # 290
You are processing a large dataset using RAPIDS cuDF and Dask-cuDF on an NVIDIA GPU. Your profiling indicates that data transfer times between CPU and GPU are significantly slowing down your pipeline.
What is the most effective way to reduce this bottleneck?

Answer: C


NEW QUESTION # 291
You are working on a medium-sized dataset (~500,000 rows, 20 columns) and need to perform fast exploratory data analysis (EDA) with filtering, aggregations, and transformations.
Which of the following Python libraries would be the most efficient choice for this task?

Answer: B


NEW QUESTION # 292
You are using RAPIDS and Dask-cuDF to process a large-scale ETL pipeline. The workflow involves multiple join and groupby operations, which are causing excessive shuffling.
How can you best optimize caching to reduce shuffle overhead?

Answer: B


NEW QUESTION # 293
You are working with cuGraph to analyze a large social network dataset where users are represented as nodes, and their connections (friendships) are represented as edges. You need to determine the most efficient way to store and process the graph in cuGraph for high-performance analytics.
Which of the following graph representations is best suited for efficient processing in cuGraph?

Answer: C


NEW QUESTION # 294
A data scientist is using RAPIDS cuML to build a predictive model on a large dataset containing numerical and categorical features.
To optimize feature engineering for accelerated GPU processing, which of the following is the best approach?

Answer: A


NEW QUESTION # 295
......

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