A brief introduction to the course

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A true simulation environment

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a large dataset (several terabytes in size) and need to perform data preprocessing, filtering, and transformations before training a machine learning model.
Given the dataset size and the requirement to optimize for GPU acceleration using NVIDIA technologies, which of the following is the most appropriate data processing library to use?

A) pandas
B) Dask DataFrame with Dask-CUDA
C) Modin with Ray backend
D) NumPy with CuPy acceleration

2. You are deploying a deep learning model on an edge device with 8GB of available RAM. The model's estimated peak memory usage, including model weights, intermediate tensors, and batch data, is 9.5GB.
What is the best course of action to ensure successful deployment while maintaining performance?

A) Reduce the batch size during inference
B) Increase the device's swap space to compensate for insufficient RAM
C) Offload some computation to cloud-based processing
D) Reduce the number of model parameters by removing layers from the architecture

3. You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

A) Using a single storage location for both raw and transformed data to simplify the workflow.
B) Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
C) Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.
D) Ensuring the ETL pipeline uses only batch processing for data ingestion.
E) Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.

4. 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) Data Understanding
B) Business Understanding
C) Data Preparation
D) Model Building

5. A data scientist is working on a dataset where the numerical features have different ranges, and they need to ensure uniformity across features before training a machine learning model.
Which of the following approaches, utilizing NVIDIA technologies, would best achieve this goal?

A) Apply cuDF's normalize() function to scale each feature between 0 and 1.
B) Apply cuML's RobustScaler() to center the data using median and scale using the interquartile range.
C) Use cuML's PCA to directly remove the need for standardization by reducing dimensionality.
D) Use cuML's StandardScaler() to transform the features to have zero mean and unit variance.

Solutions: