You are tasked with optimizing an ETL pipeline that processes petabytes of data daily. Your organization is already using Apache Spark for distributed data processing but is experiencing performance bottlenecks. You need a solution that improves execution speed without requiring extensive code modifications.
Which of the following solutions best meets your needs?

• A. Using Apache Arrow to optimize in-memory columnar processing within Spark
• B. Switching to Dask and RAPIDS to fully utilize GPU acceleration
• C. Using the NVIDIA Spark RAPIDS Accelerator to run Spark workloads on GPUs
• D. Rewriting the entire ETL pipeline in CUDA to maximize GPU efficiency

You need to generate synthetic data to augment an imbalanced dataset using RAPIDS™ and cuDF.
Which of the following strategies would be most effective in producing high-quality synthetic data for the minority class?

• A. Use only the majority class data to train a model and generate synthetic data using a GAN (Generative Adversarial Network) in the RAPIDS ecosystem.
• B. Use synthetic data generation libraries like SDV (Synthetic Data Vault) in conjunction with cuDF to create synthetic data that mimics the distribution of the minority class.
• C. Create synthetic data by applying random transformations to the minority class, such as scaling, rotation, or flipping, using cuDF.
• D. Generate synthetic data by duplicating entries from the minority class using cudf.DataFrame.sample().

When comparing the required memory with the available memory on a GPU for an MLOps deployment using NVIDIA technologies, which of the following is the best method to optimize memory usage while training large models?

• A. Increase the input data size to fully utilize available memory and improve training performance.
• B. Use mixed-precision training to reduce memory requirements by using half-precision floating-point numbers.
• C. Use a higher number of GPUs to distribute the model and memory load across the GPUs.
• D. Decrease the number of training iterations to reduce memory consumption.

You are working with a dataset containing hundreds of millions of records, and you need to perform ETL operations such as filtering, joins, and aggregations. Given the dataset size, which NVIDIA- accelerated library should you use to achieve optimal performance?

• A. Pandas, as it is widely used and supports all common DataFrame operations, even for very large datasets.
• B. cuPy, because it provides GPU-accelerated array operations, making it the best option for processing tabular data.
• C. NumPy, because it is optimized for numerical computing and offers better performance for handling tabular data.
• D. cuDF, as it provides GPU-accelerated DataFrame operations similar to Pandas, allowing for efficient processing of large datasets.

You need to set up an isolated, GPU-accelerated environment for a deep learning project that requires specific CUDA, cuDNN, and RAPIDS versions.
Which of the following best ensures a reproducible environment using Docker?

• A. Install NVIDIA drivers manually inside a Docker container every time it runs.
• B. Use the nvidia/cuda base image and specify the required RAPIDS and deep learning libraries in a Dockerfile.
• C. Build a container from an Ubuntu base image and manually install all dependencies without specifying versions.
• D. Use a system-wide CUDA installation and mount the /usr/local/cuda directory into the container to provide GPU support.