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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. When comparing the required memory with the available memory on a GPU for an MLOps deployment using NVIDIA technologies, which of the following is the best method to optimize memory usage while training large models?

A) Use a higher number of GPUs to distribute the model and memory load across the GPUs.
B) Use mixed-precision training to reduce memory requirements by using half-precision floating-point numbers.
C) Decrease the number of training iterations to reduce memory consumption.
D) Increase the input data size to fully utilize available memory and improve training performance.

2. A data scientist is using NVIDIA RAPIDS to perform statistical analysis as part of exploratory data analysis (EDA) on a dataset containing millions of product reviews. They need to compute basic descriptive statistics such as mean, median, and variance efficiently.
Which of the following methods is the most appropriate for performing these calculations on GPUs?

A) Use cuDF's built-in statistical functions like .mean(), .median(), and .var()
B) Convert the dataset into a PyTorch tensor and use PyTorch's statistical methods
C) Use a traditional SQL database to compute statistics and then transfer results to the GPU
D) Use NumPy's statistical functions, such as numpy.mean() and numpy.var()

3. Which of the following actions can you perform using DLProf to analyze a deep learning model's performance?

A) Automatically adjust the learning rate based on the model's convergence
B) Modify the training dataset during model execution
C) Visualize GPU memory utilization over time
D) Increase batch size to improve accuracy

4. Which of the following best describes the role of MLOps in the context of NVIDIA technologies for deploying machine learning models in production? (Select two)

A) MLOps replaces the need for data preprocessing during training and deployment
B) MLOps ensures that models trained on GPUs can only run on GPUs during deployment
C) MLOps frameworks support version control and automation, ensuring reproducibility and scalability of ML workflows
D) MLOps helps manage the lifecycle of machine learning models, ensuring efficient collaboration and model governance

5. You are working with a dataset in a cloud-based GPU environment that contains a column country representing the country of origin for customers. The column contains only 10 unique country values, but the dataset has millions of rows.
Which of the following is the most memory-efficient approach to handle the country column in a cuDF DataFrame?

A) df['country'] = df['country'].astype('object')
B) df['country'] = df['country'].astype('int32')
C) df['country'] = df['country'].astype('category')
D) df['country'] = df['country'].astype('string')

Solutions: