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NCP-ADS Exam Course, NCP-ADS Pdf Braindumps, NCP-ADS Valid Test Tips, NCP-ADS Test Engine Version, NCP-ADS Valid Dumps Book

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q220-Q225):

NEW QUESTION # 220
In Python, when working with large datasets using pandas, which of the following methods are best for improving performance and efficiency when applying operations on DataFrames? (Select two)

  • A. Using vectorized operations (e.g., element-wise arithmetic)
  • B. Using for loops to apply operations row by row
  • C. Using map() function to apply a function element-wise
  • D. Using iterrows() for iterating through DataFrame rows
  • E. Using apply() function over DataFrame rows

Answer: A,C


NEW QUESTION # 221
Which of the following is the main advantage of using TensorRT for inference in an accelerated data science pipeline?

  • A. TensorRT is only compatible with image classification models and does not support other model types.
  • B. TensorRT is mainly used for data visualization and not for model inference.
  • C. TensorRT optimizes deep learning models to run efficiently on NVIDIA GPUs by reducing precision while maintaining accuracy.
  • D. TensorRT automatically builds training models from raw data without requiring pre-trained models.

Answer: C


NEW QUESTION # 222
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?

  • A. NVIDIA Triton Inference Server to preprocess the dataset before model training.
  • B. NVIDIA RAPIDS cuDF to transform image data into tabular format for analysis.
  • C. NVIDIA DALI (Data Loading Library) to accelerate data loading and preprocessing on the GPU.
  • D. NVIDIA Nsight Compute to optimize image dataset processing.

Answer: C


NEW QUESTION # 223
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?

  • A. NVIDIA DALI can offload image preprocessing tasks to the GPU, reducing CPU bottlenecks and improving training throughput.
  • B. NVIDIA DALI is an alternative to cuDF for GPU-accelerated tabular data processing.
  • C. NVIDIA DALI only supports preprocessing images and does not include any functionality for video or text data.
  • D. NVIDIA DALI does not support data augmentation and is only used for raw data loading.

Answer: A


NEW QUESTION # 224
You are designing a reproducible benchmark to compare the performance of deep learning models across frameworks like PyTorch and TensorFlow using NVIDIA's A100 GPU.
Which step is most critical in ensuring fair benchmarking conditions?

  • A. Enabling XLA compiler optimizations only for TensorFlow to enhance its performance.
  • B. Ensuring the same CUDA/cuDNN and driver versions are installed when running benchmarks across frameworks.
  • C. Measuring only forward pass latency to compare inference speed while ignoring backward pass computation.
  • D. Using a different precision setting for each framework to maximize performance per framework's capabilities.

Answer: B


NEW QUESTION # 225
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