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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a time-series forecasting project using NVIDIA RAPIDS and GPU-accelerated machine learning. The dataset consists of 10 years of daily stock price data. Your goal is to implement a model that efficiently handles large-scale time-series data while leveraging GPU acceleration for optimal performance.
Which approach best utilizes NVIDIA technologies for efficient forecasting?

A) Use cuDF for data preprocessing and train an XGBoost model with GPU acceleration for forecasting.
B) Use Dask with pandas for data preprocessing, then train a TensorFlow LSTM model on the CPU.
C) Use cuDF to load and preprocess the data, then apply FB Prophet for forecasting.
D) Use PyTorch with CPU acceleration to train a convolutional neural network (CNN) for forecasting.

2. Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A) Robust Scaling
B) Z-score Standardization
C) Log Transformation
D) Min-Max Scaling

3. You are training a convolutional neural network (CNN) model with a large dataset on a single GPU.
The model is consuming too much GPU memory, and training is slow.
Which of the following techniques would help you reduce GPU memory consumption while maintaining or improving the efficiency of training? (Select two)

A) Use a smaller batch size to reduce the memory requirements per training iteration, allowing for faster training.
B) Implement mixed precision training to lower memory usage and speed up computation without losing accuracy.
C) Use batch normalization to reduce memory usage by skipping some of the computations during training.
D) Use automatic differentiation for every operation to save memory during the backpropagation phase.
E) Decrease the size of the model by reducing the number of layers or the number of filters per layer.

4. You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?

A) NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.
B) NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.
C) NVIDIA Nsight Systems for managing dataset storage and retrieval performance.
D) NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.

5. You are tasked with acquiring a dataset for training a machine learning model in healthcare, predicting patient readmission rates. Before using the dataset, you must assess its quality.
Which of the following is the most important factor to evaluate before acquisition?

A) The number of missing values and inconsistencies in key columns
B) The file format (CSV, JSON, or XML) of the dataset
C) Whether the dataset is stored on a cloud server or a local machine
D) The programming language used to preprocess the dataset

Solutions: