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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a large dataset containing missing values, and you need to clean and preprocess the data efficiently.
Which of the following methods provides the best performance when handling missing values in a GPU- accelerated EDA workflow using RAPIDS?

A) Use pandas.DataFrame.fillna() on the dataset before converting it to a cuDF DataFrame.
B) Ignore missing values since GPU acceleration can handle incomplete data without performance degradation.
C) Convert the dataset to a cuDF DataFrame and use cudf.DataFrame.fillna() to fill missing values.
D) Drop all missing values using df.dropna() in Pandas before using RAPIDS.

2. A data scientist is working on a customer segmentation model using NVIDIA RAPIDS on GPUs. The dataset contains millions of customer records with features such as transaction history, age, location, and frequency of visits.
To optimize feature engineering using NVIDIA technologies, what is the best approach?

A) Convert all categorical variables into string representations to preserve their original format for later analysis.
B) Use cuDF to perform feature transformations like normalization and one-hot encoding directly on the GPU.
C) Perform feature engineering on CPUs using pandas before transferring data to the GPU for training.
D) Store all numerical features in float64 format to prevent rounding errors during transformations.

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

4. You are building a predictive model for retail sales forecasting and need a dataset that includes historical sales transactions, customer demographics, and external economic indicators (e.g., inflation rate, unemployment rate).
Which of the following datasets would be the most appropriate for your model?

A) A public dataset of annual GDP per country
B) A dataset with global temperature trends over the past decade
C) A dataset containing transaction history and customer profiles from a retail company
D) A dataset of product reviews and customer sentiments from an e-commerce website

5. You need to train a deep learning model using PyTorch on a dataset too large for a single GPU. You decide to use Dask with NVIDIA GPUs for multi-GPU scaling.
Which approach is the most effective for distributing the workload?

A) Use Dask.delayed to wrap PyTorch training functions and schedule them across multiple GPUs
B) Use Dask-CUDA workers with PyTorch's DistributedDataParallel (DDP) for training across multiple GPUs
C) Use Dask's built-in deep learning API to automatically distribute PyTorch models across GPUs
D) Use Dask Bag to shard the dataset and train separate PyTorch models on each shard

Solutions: