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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q280-Q285):

NEW QUESTION # 280
A data scientist is working on a customer segmentation model using NVIDIA RAPIDS on GPUs. The dataset contains millions of customer records with features such as transaction history, age, location, and frequency of visits.
To optimize feature engineering using NVIDIA technologies, what is the best approach?

Answer: C


NEW QUESTION # 281
You are tasked with processing a large dataset of 100 million records for a deep learning project using NVIDIA technologies. You need to determine the most efficient data processing library for this task to maximize performance and reduce processing time.
Which of the following libraries is best suited for this task?

Answer: B


NEW QUESTION # 282
A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

Answer: B


NEW QUESTION # 283
A data engineer is tasked with processing a 5 TB dataset stored in Apache Parquet format. The dataset consists of user activity logs and needs to be filtered, aggregated, and processed for feature engineering before training an ML model. The engineer is deciding between Dask and Apache Spark.
Which statement best describes a key difference between the two frameworks?

Answer: A


NEW QUESTION # 284
You are deploying an NVIDIA GPU-accelerated machine learning model in a Docker container and want to ensure that your application can leverage the GPU efficiently.
What is the best way to manage CUDA dependencies and avoid compatibility issues inside your Docker container?

Answer: C


NEW QUESTION # 285
......

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