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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You have a massive time-series dataset containing millions of records per day, and you need to perform forecasting at scale.
Which of the following techniques best utilizes NVIDIA technologies to optimize time-series forecasting?

A) Use RAPIDS cuML's XGBoost-GPU implementation to forecast time-series patterns with improved training efficiency.
B) Convert time-series data to tensors and train a deep learning model using PyTorch with mixed precision on an NVIDIA GPU.
C) Use RAPIDS cuDF and Dask-cuDF to preprocess the data and train an LSTM model with PyTorch Lightning on multiple GPUs.
D) Run Facebook Prophet on a multi-core CPU environment to leverage parallel forecasting.

2. Which of the following best describes the role of MLOps in the context of NVIDIA technologies for deploying machine learning models in production? (Select two)

A) MLOps replaces the need for data preprocessing during training and deployment
B) MLOps ensures that models trained on GPUs can only run on GPUs during deployment
C) MLOps frameworks support version control and automation, ensuring reproducibility and scalability of ML workflows
D) MLOps helps manage the lifecycle of machine learning models, ensuring efficient collaboration and model governance

3. You are tasked with optimizing the performance of an MLOps pipeline that uses GPU-accelerated workflows. After running initial benchmarks, you notice that the training time is higher than expected, despite the use of multiple GPUs.
What are the best strategies to optimize the GPU-accelerated workflow in this case? (Select two)

A) Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.
B) Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.
C) Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.
D) Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.
E) Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.

4. You are working on a data science project where you need to process a large dataset containing
500 million records. You want to determine whether GPU acceleration would significantly improve performance.
Which of the following factors best indicates that you should use an accelerated computing solution like RAPIDS?

A) The dataset is a structured table with less than 100,000 records and can be handled efficiently with a Pandas DataFrame.
B) The dataset is heavily structured but mainly requires text-based analysis using regex-based search and manipulation.
C) The dataset consists of simple arithmetic operations on a few columns and can be processed using vectorized NumPy operations.
D) The dataset has high-dimensional sparse features and requires complex operations such as nearest neighbor search and clustering.

5. Which of the following is the main advantage of using TensorRT for inference in an accelerated data science pipeline?

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

Solutions: