NVIDIA NCP-ADS : NVIDIA-Certified-Professional Accelerated Data Science

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are deploying an NVIDIA GPU-accelerated machine learning model in a Docker container and want to ensure that your application can leverage the GPU efficiently.
What is the best way to manage CUDA dependencies and avoid compatibility issues inside your Docker container?

A) Use NVIDIA's official Docker images from NVIDIA GPU Cloud (NGC), which come with pre-installed CUDA and AI frameworks.
B) Manually install CUDA and cuDNN inside the container by downloading them from NVIDIA's website and setting environment variables.
C) Use a base Ubuntu image and install TensorFlow, PyTorch, and CUDA using pip install inside the container.
D) Disable GPU acceleration in Docker and force computations on the CPU to avoid CUDA compatibility issues.

2. A data scientist is processing a dataset that is too large to fit into the memory of a single GPU. They decide to use Dask with cuDF to leverage multiple GPUs for accelerated computation.
Which of the following approaches ensures efficient parallelism when working with dask_cudf?

A) Convert the dataset into a Dask cuDF dataframe and use persist() to keep it in GPU memory.
B) Assign explicit chunks of data to each worker by manually dividing the dataset before passing it to Dask.
C) Use a single-threaded scheduler to avoid contention when running on multiple GPUs.
D) Use Dask futures to asynchronously submit GPU tasks and collect results when completed.

3. You are developing an AI model for medical imaging that requires acquiring a large dataset of MRI scans from multiple sources.
Which NVIDIA technology would best assist in acquiring, standardizing, and efficiently handling the dataset?

A) Use NVIDIA Nsight Systems to collect MRI data and visualize potential performance bottlenecks.
B) Use NVIDIA Clara Imaging to standardize, curate, and preprocess MRI datasets for AI model training.
C) Use NVIDIA AI Enterprise to directly acquire and store MRI images from multiple hospitals.
D) Use NVIDIA Morpheus to analyze MRI data for cybersecurity threats before storing it.

4. You are training a deep learning model for image classification and want to optimize its hyperparameters, including learning rate, batch size, and number of layers.
Which of the following techniques is the most effective for efficiently searching through a high- dimensional hyperparameter space?

A) Bayesian Optimization
B) Gradient Descent
C) Grid Search
D) Random Search

5. A data scientist is working with large-scale ETL (Extract, Transform, Load) pipelines on GPU- accelerated infrastructure using RAPIDS. The workload involves frequent shuffle operations, which significantly impact performance.
What is the best approach using NVIDIA technologies to reduce shuffle overhead and improve performance?

A) Use RAPIDS cuDF's GPU memory caching to store intermediate DataFrames and avoid redundant shuffle operations.
B) Enable CUDA Unified Memory to automatically optimize shuffle performance without manual intervention.
C) Store all intermediate shuffle data in CPU memory using pandas to ensure persistence and reduce GPU load.
D) Use RAPIDS cuML to replace shuffle-intensive operations with an ML model that predicts data distribution.

Solutions: