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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question #1
A machine learning engineer is working on a dataset with thousands of numerical features. The dataset is too large for standard CPU-based processing, so the engineer decides to leverage GPUs for efficient feature engineering.
Which of the following techniques is the most suitable for dimensionality reduction using GPU acceleration?

A. Encoding all numerical features as categorical variables to reduce dimensionality.
B. Using manual feature selection by dropping columns with low variance on a CPU.
C. Converting the dataset into a sparse matrix and running traditional singular value decomposition (SVD) on a CPU.
D. Applying RAPIDS cuML's PCA (Principal Component Analysis) to reduce feature dimensions efficiently.

Question #2
You are working on a large dataset for a machine learning model that will be trained using RAPIDS cuML. The dataset includes categorical, integer, and floating-point features.
Which of the following approaches is the best practice for determining the optimal data type choice for each feature using NVIDIA's RAPIDS cuDF library?

A. Use float16 for all floating-point data to reduce memory usage and increase GPU processing speed.
B. Convert all numerical data to float64 for maximum precision in calculations.
C. Convert categorical variables into int8 to optimize GPU memory usage.
D. Use float32 instead of float64 for floating-point numbers when possible, and leverage int8, int16, or int32 for categorical and integer data based on their range.

Question #3
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. Deploy the model to an edge device using TensorRT for real-time inference.
B. Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.
C. Define the problem statement and collect relevant datasets before training the model.
D. Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().

Question #4
A machine learning engineer is working on an image classification problem where the dataset is small and lacks variability. To improve generalization, the engineer decides to augment the dataset using NVIDIA RAPIDS.
What is the best method to generate synthetic data efficiently while leveraging GPU acceleration?

A. Use cuML.PCA() to reduce dimensionality and create synthetic samples by reconstructing the data with added noise.
B. Use cuDF with cudf.DataFrame.sample() to create new samples by randomly selecting existing rows.
C. Use traditional CPU-based augmentation techniques like OpenCV to transform images and generate new data.
D. Apply cuML.GaussianMixture() to generate new synthetic data points based on an estimated probability distribution.

Question #5
You are tasked with optimizing an ETL pipeline that processes petabytes of data daily. Your organization is already using Apache Spark for distributed data processing but is experiencing performance bottlenecks. You need a solution that improves execution speed without requiring extensive code modifications.
Which of the following solutions best meets your needs?

A. Using the NVIDIA Spark RAPIDS Accelerator to run Spark workloads on GPUs
B. Switching to Dask and RAPIDS to fully utilize GPU acceleration
C. Rewriting the entire ETL pipeline in CUDA to maximize GPU efficiency
D. Using Apache Arrow to optimize in-memory columnar processing within Spark

Solutions: