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

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

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

2. A retail company is deploying an AI-driven demand forecasting system using NVIDIA GPUs. The team follows the CRISP-DM framework and is currently in the Evaluation phase.
Which approach best leverages NVIDIA technologies to assess model performance effectively?

A) Perform evaluation on a small CPU-based subset of the dataset instead of using full GPU-accelerated inference.
B) Use RAPIDS cuML to rapidly compute evaluation metrics like RMSE and R-squared on large datasets using GPUs.
C) Rely only on training loss as the primary evaluation metric without considering validation performance.
D) Assume that a high training accuracy guarantees excellent real-world performance, skipping the evaluation phase.

3. You are working on an MLOps pipeline that involves loading a large dataset for training a deep learning model on an NVIDIA GPU. Before training, you need to ensure that the dataset fits within the available GPU memory.
Which of the following commands in Python using the pandas and numpy libraries can correctly determine the memory size of a dataset?

A) df.memory_usage(deep=True).sum()
B) df.info(memory_usage='deep')
C) sys.getsizeof(df)
D) np.array(df).nbytes

4. Which of the following methods are commonly used to handle missing data in data analysis? (Select two)

A) Removing rows with missing data
B) Ignoring missing data entirely
C) Imputing with the mean or median of the feature
D) Using a fixed constant value for imputation
E) Using the mode to impute missing values

5. You are setting up a GPU-accelerated data science environment that includes NVIDIA RAPIDS, PyTorch, TensorFlow, and other libraries for machine learning and data processing.
Given that these frameworks have different dependencies and version requirements, what is the best approach to avoid software conflicts while ensuring reproducibility across multiple environments?

A) Use a single Docker container with the latest versions of all dependencies installed system-wide.
B) Use Conda to create isolated virtual environments for each project and install dependencies via conda-forge or NVIDIA channels.
C) Install all packages globally using pip on the system-wide Python installation to ensure consistency.
D) Manually download and compile each library from source to guarantee compatibility across all versions.

Solutions: