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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You have trained a machine learning model using cuML as part of the Modeling phase in the CRISP- DM framework. Now, you need to assess how well the model performs before moving forward with deployment.
Which of the following steps aligns best with the Evaluation phase of CRISP-DM using NVIDIA technologies?

A) Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().
B) Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.
C) Deploy the model to an edge device using TensorRT for real-time inference.
D) Define the problem statement and collect relevant datasets before training the model.

2. You need to set up an isolated, GPU-accelerated environment for a deep learning project that requires specific CUDA, cuDNN, and RAPIDS versions.
Which of the following best ensures a reproducible environment using Docker?

A) Use the nvidia/cuda base image and specify the required RAPIDS and deep learning libraries in a Dockerfile.
B) Use a system-wide CUDA installation and mount the /usr/local/cuda directory into the container to provide GPU support.
C) Build a container from an Ubuntu base image and manually install all dependencies without specifying versions.
D) Install NVIDIA drivers manually inside a Docker container every time it runs.

3. 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) Rely on Matplotlib to generate static plots and update them every minute with a loop.
D) Convert the stock price dataset into a NumPy array and visualize it using Seaborn's line plot.

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

5. Which of the following is the most appropriate way to perform large-scale data processing in a GPU- accelerated environment using NVIDIA RAPIDS?

A) Use NumPy exclusively for processing large datasets on GPUs.
B) Use Dask on top of RAPIDS for distributed computing across multiple GPUs.
C) Use pandas for all data manipulations and rely on multi-threading for parallel execution.
D) Use TensorFlow for all data manipulations in a GPU environment.

Solutions: