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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a deep learning project that requires a large dataset of high-resolution satellite images for training a convolutional neural network (CNN). You want to leverage NVIDIA technologies to efficiently acquire and manage the dataset.
Which of the following approaches is the most suitable?

A) Use NVIDIA DALI (Data Loading Library) to stream and preprocess satellite image data efficiently for deep learning training.
B) Use NVIDIA RAPIDS cuDF to directly download and preprocess satellite images from an API in real time.
C) Use NVIDIA DeepStream to acquire satellite images and store them in a structured dataset for machine learning.
D) Use NVIDIA Modulus to generate synthetic satellite images instead of acquiring real-world data.

2. A data science team is using NVIDIA GPUs to accelerate their AI workflow for a fraud detection system. They follow the CRISP-DM methodology to ensure a structured approach to the project.
During the Data Preparation phase, what is the most effective way to leverage NVIDIA technologies?

A) Perform all data transformation on a single CPU core to ensure stability before deploying to GPU- accelerated training.
B) Ignore feature engineering and feed raw data directly into the model, relying on deep learning to extract relevant features.
C) Use NVIDIA RAPIDS to preprocess large-scale transaction datasets efficiently on GPUs before training the model.
D) Manually clean and transform data using traditional CPU-based processing tools like pandas, then transfer the data to the GPU only for training.

3. Which of the following are key advantages of using cuGraph for analyzing graph data in GPU- accelerated environments? (Select two)

A) cuGraph only works on small-scale graph datasets that can fit into memory.
B) cuGraph supports various graph algorithms, including PageRank, shortest path, and community detection, leveraging GPU parallelism.
C) cuGraph only works with cloud-based computing environments and is not optimized for local GPUs.
D) cuGraph can efficiently handle larger graphs than traditional CPU-based methods, providing significant performance improvements.
E) cuGraph does not support distributed graph processing and is only suitable for single-node systems.

4. You are working with a GPU-based cloud environment and need to optimize the memory usage for a dataset that contains a column item_id representing unique product IDs. The item_id values are large integers, and there are over 10 million distinct product IDs.
Which of the following is the most memory-efficient data type choice for this column?

A) df['item_id'] = df['item_id'].astype('string')
B) df['item_id'] = df['item_id'].astype('int32')
C) df['item_id'] = df['item_id'].astype('float64')
D) df['item_id'] = df['item_id'].astype('int64')

5. Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?

A) Model Building
B) Data Preparation
C) Business Understanding
D) Data Understanding

Solutions: