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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. 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 setups perform better only when the batch size is reduced.
B) Single-GPU training is generally more cost-effective and should be preferred unless scaling is absolutely necessary.
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) Multi-GPU training requires modifications to the model architecture to make it compatible with parallel processing.
E) Multi-GPU training can significantly reduce training time when the dataset is large and the model is computationally intensive.

2. Which of the following is the primary benefit of using NVIDIA RAPIDS in the context of data science workflows?

A) It allows for the implementation of reinforcement learning algorithms
B) It simplifies the creation of visualizations for large datasets
C) It enables real-time data streaming and analytics
D) It speeds up the preprocessing and analysis of large datasets by utilizing GPUs

3. You are working with a large dataset containing millions of high-resolution images for a deep learning project. The dataset needs to be processed efficiently on a GPU before training a model.
Which NVIDIA technology is best suited for preprocessing, augmenting, and efficiently loading the dataset into memory?

A) NVIDIA Triton Inference Server to preprocess the dataset before model training.
B) NVIDIA Nsight Compute to optimize image dataset processing.
C) NVIDIA DALI (Data Loading Library) to accelerate data loading and preprocessing on the GPU.
D) NVIDIA RAPIDS cuDF to transform image data into tabular format for analysis.

4. A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

A) Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
B) Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.
C) Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.
D) Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.

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

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