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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.
Which NVIDIA-supported tool is best suited for this purpose?

A) Precompute the time-series visualization with Dask and display it in a static HTML page.
B) Use Plotly Dash with RAPIDS cuDF to create an interactive GPU-powered dashboard.
C) Convert the stock price dataset into a NumPy array and visualize it using Seaborn's line plot.
D) Rely on Matplotlib to generate static plots and update them every minute with a loop.

2. Which of the following best describes a key advantage of using cloud-based GPU instances for machine learning model training?

A) Cloud-based GPU instances offer lower latency and better network performance compared to on- premise deployments, regardless of geographical location.
B) Cloud GPUs provide dynamically scalable resources, allowing users to increase or decrease compute power based on demand without upfront hardware investment.
C) Cloud GPUs are always more cost-effective than on-premise GPUs, as they do not incur long-term usage costs.
D) Cloud GPU instances cannot support containerized workloads, limiting their applicability for MLOps and CI/CD pipelines.

3. You are using NVIDIA DLProf to analyze the performance of a deep learning model deployed on an A100 GPU. The report indicates that compute-bound operations are dominating execution time, and kernel execution efficiency is below 50%.
What is the best action to take based on this insight?

A) Enable mixed precision training to improve computational efficiency.
B) Use DLProf's Tensor Core Analysis to check if the model is leveraging Tensor Cores effectively.
C) Increase the batch size to fully utilize available GPU memory and reduce per-sample processing overhead.
D) Reduce the number of layers in the model to decrease computation time.

4. You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.
Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?

A) df = cudf.read_parquet("data.parquet")
B) df = cudf.read_csv("data.parquet")
C) df = pd.read_parquet("data.parquet")
D) df = cudf.to_gpu(pd.read_parquet("data.parquet"))

5. Which tools or technologies from NVIDIA are essential for implementing an efficient MLOps pipeline in production environments? (Select two)

A) NVIDIA TensorRT for efficient model inference
B) NVIDIA NGC for storing and sharing machine learning datasets
C) NVIDIA Triton Inference Server for managing deployment and serving models
D) NVIDIA DLA (Deep Learning Accelerator) for model deployment
E) NVIDIA CUDA for model training in cloud environments

Solutions: