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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are tasked with designing a benchmark to compare the performance of different GPU- accelerated machine learning frameworks, such as TensorFlow, PyTorch, and RAPIDS.
Which of the following factors is the most critical to ensure a fair and meaningful comparison?

A) Ensuring all frameworks use the same dataset, batch size, and GPU hardware during benchmarking.
B) Using different model architectures per framework to test a variety of scenarios and ensure a broad evaluation.
C) Using the latest software versions of each framework, regardless of compatibility with the GPU hardware.
D) Allowing each framework to run with its default settings, as tuning hyperparameters would bias the results.

2. Which of the following is the primary benefit of using NVIDIA RAPIDS in the context of data science workflows?

A) It simplifies the creation of visualizations for large datasets
B) It speeds up the preprocessing and analysis of large datasets by utilizing GPUs
C) It allows for the implementation of reinforcement learning algorithms
D) It enables real-time data streaming and analytics

3. A machine learning engineer is training a convolutional neural network (CNN) on an NVIDIA GPU and needs to maximize throughput while avoiding OOM errors.
Which of the following techniques is the most effective way to balance memory efficiency and training speed?

A) Using a batch size of 1 to minimize memory usage
B) Loading all dataset samples into GPU memory at the start of training
C) Allocating a fixed batch size without monitoring memory usage
D) Using dynamic batch sizing based on available GPU memory

4. You are working on a machine learning dataset with millions of rows and want to optimize performance on a GPU using CuDF. One of the features in your dataset represents categorical data with a limited number of unique values.
Which of the following data types should you use to maximize performance while reducing memory usage?

A) int64
B) object (string)
C) bool
D) category

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

Solutions: