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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is analyzing a large time-series dataset containing stock price movements of thousands of companies over a decade. The dataset is stored as a cuDF DataFrame and contains millions of rows. The scientist wants to visualize trends and patterns interactively while leveraging GPU acceleration.
Which of the following approaches is the most efficient for visualizing this time-series data?

A) Use matplotlib with plt.plot() while applying df.to_pandas() to convert data.
B) Convert the cuDF DataFrame to Pandas and use matplotlib for plotting.
C) Use cuXfilter with a cuDF DataFrame to generate interactive visualizations directly on the GPU.
D) Use seaborn with a sampled subset of the dataset to generate line plots.

2. You are training a deep learning model on a large dataset and are deciding whether to use a single GPU or multiple GPUs.
Which of the following are true considerations when comparing single-GPU and multi-GPU training setups? (Select two)

A) Multi-GPU training can significantly reduce training time when the dataset is large and the model is computationally intensive.
B) Multi-GPU training requires modifications to the model architecture to make it compatible with parallel processing.
C) Single-GPU training is limited by the VRAM (video memory) on the GPU, so larger models or datasets may require multi-GPU setups.
D) Single-GPU training is generally more cost-effective and should be preferred unless scaling is absolutely necessary.
E) Multi-GPU setups perform better only when the batch size is reduced.

3. A data scientist is working on a customer segmentation model using NVIDIA RAPIDS on GPUs. The dataset contains millions of customer records with features such as transaction history, age, location, and frequency of visits.
To optimize feature engineering using NVIDIA technologies, what is the best approach?

A) Store all numerical features in float64 format to prevent rounding errors during transformations.
B) Convert all categorical variables into string representations to preserve their original format for later analysis.
C) Perform feature engineering on CPUs using pandas before transferring data to the GPU for training.
D) Use cuDF to perform feature transformations like normalization and one-hot encoding directly on the GPU.

4. A machine learning engineer is working on an image classification problem where the dataset is small and lacks variability. To improve generalization, the engineer decides to augment the dataset using NVIDIA RAPIDS.
What is the best method to generate synthetic data efficiently while leveraging GPU acceleration?

A) Apply cuML.GaussianMixture() to generate new synthetic data points based on an estimated probability distribution.
B) Use traditional CPU-based augmentation techniques like OpenCV to transform images and generate new data.
C) Use cuDF with cudf.DataFrame.sample() to create new samples by randomly selecting existing rows.
D) Use cuML.PCA() to reduce dimensionality and create synthetic samples by reconstructing the data with added noise.

5. Which of the following Nvidia technologies is primarily used for performing benchmarking and optimizing GPU-accelerated deep learning workflows, especially focusing on model training performance?

A) Nvidia DeepStream SDK
B) Nvidia CUDA
C) Nvidia Triton Inference Server
D) Nvidia Nsight Systems

Solutions: