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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q169-Q174):

NEW QUESTION # 169
A data science team is deploying a deep learning model for real-time inference. The model is optimized for inference on an NVIDIA A100 GPU, but the team notices that inference latency is higher than expected.
Which of the following optimizations is most effective in reducing inference latency?

Answer: B


NEW QUESTION # 170
You are working on a data science project that involves processing large-scale financial transaction data. You want to optimize data manipulation operations using NVIDIA's RAPIDS cuDF.
Which of the following approaches best leverages NVIDIA technologies for efficient data manipulation?

Answer: C


NEW QUESTION # 171
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: D


NEW QUESTION # 172
A machine learning team is handling large-scale datasets that need to be efficiently stored and accessed within an NVIDIA RAPIDS workflow.
Which of the following storage formats and techniques provides the best performance for GPU-based data science pipelines?

Answer: B


NEW QUESTION # 173
A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.
Which NVIDIA-supported tool is best suited for this purpose?

Answer: D


NEW QUESTION # 174
......

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