Free NVIDIA NCP-ADS Exam Dumps Questions & Answers

You are training a deep learning model on a large dataset of images stored in an Amazon S3 bucket.
You want to optimize data loading, augmentation, and preprocessing on NVIDIA GPUs to avoid CPU bottlenecks.
Which of the following approaches is the most efficient for GPU-accelerated data preprocessing?

• A.  Load the dataset using PyTorch's torchvision.transforms and DataLoader, leveraging the CPU for data preprocessing and transferring batches to the GPU before training.
• B.  Use NVIDIA DALI to decode images, apply transformations such as resizing and normalization, and load batches directly to the GPU for training.
• C.  Use OpenCV to load and preprocess images on the CPU, then transfer the processed images to the GPU before training.
• D.  Use TensorFlow's tf.data API with tf.image transformations and ensure that the preprocessed images are transferred to GPU memory at the end of the pipeline.

You are working with large datasets in cuDF and have noticed significant performance bottlenecks due to repeated computation and excessive shuffling in your workflow. You want to use data caching to optimize the execution plan and reduce redundant operations.
Which of the following is the best way to implement data caching in cuDF to avoid repeated recomputation and excessive shuffling?

• A.  Use the .persist() method on a cuDF DataFrame to store intermediate results in GPU memory, reducing redundant calculations.
• B.  Store intermediate DataFrames as temporary CSV files on disk and reload them when needed to simulate a cache.
• C.  Use the .reset_index() method after performing transformations to force materialization of the dataset and cache results.
• D.  Use the .to_pandas() method to convert the cuDF DataFrame into a Pandas DataFrame, leveraging CPU caching instead of GPU caching.

A data scientist is working on training a deep learning model in a cloud-based environment. The dataset is large, and model convergence is taking too long on a standard CPU instance.
To optimize performance through GPU acceleration, which of the following strategies should the data scientist implement?

• A.  Disable CUDA and use only OpenMP to parallelize computations across CPU cores.
• B.  Store all training data in RAM and load it directly to the CPU for processing.
• C.  Use a cloud instance with multiple GPUs and enable mixed-precision training.
• D.  Increase the number of CPU cores and distribute training across multiple CPU threads.

A data scientist is working on a dataset where the numerical features have different ranges, and they need to ensure uniformity across features before training a machine learning model.
Which of the following approaches, utilizing NVIDIA technologies, would best achieve this goal?

• A.  Use cuML's StandardScaler() to transform the features to have zero mean and unit variance.
• B.  Apply cuDF's normalize() function to scale each feature between 0 and 1.
• C.  Use cuML's PCA to directly remove the need for standardization by reducing dimensionality.
• D.  Apply cuML's RobustScaler() to center the data using median and scale using the interquartile range.

A data engineering team is tasked with processing terabytes of log data every hour using an ETL pipeline. Due to the large data volume, they need a scalable GPU-accelerated solution that can distribute data processing across multiple GPUs.
Which approach best meets their needs?

• A.  Process data using Pandas, then export the results to a CSV file for GPU-accelerated analytics.
• B.  Use NumPy for data transformations before converting the dataset into cuDF for final storage.
• C.  Use cuDF alone for processing log data, as it provides optimal performance on a single GPU.
• D.  Use Dask-cuDF to distribute cuDF DataFrame operations across multiple GPUs, enabling parallel ETL processing.