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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a structured dataset of around 10GB and need to perform exploratory data analysis (EDA), feature engineering, and filtering operations efficiently using NVIDIA technologies. The dataset fits into a single GPU's memory.
Which data processing library should you use to achieve the best performance?

A) cuDF
B) Spark with RAPIDS Accelerator
C) pandas
D) Dask DataFrame with Dask-CUDA

2. You are working with a large dataset on an NVIDIA GPU, where optimizing memory usage is a priority. Your dataset contains a column, transaction_id, which stores unique integer values ranging between 0 and 100,000.
Which of the following data types is the most memory-efficient choice for this column in cuDF?

A) df['transaction_id'] = df['transaction_id'].astype('float32')
B) df['transaction_id'] = df['transaction_id'].astype('int64')
C) df['transaction_id'] = df['transaction_id'].astype('int8')
D) df['transaction_id'] = df['transaction_id'].astype('int32')

3. You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?

A) Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution
B) Drop all missing values using .dropna() instead of filling them, as GPU memory is limited
C) Use cuDF's .fillna() method to replace missing values in GPU memory
D) Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF

4. You are building a deep learning model using TensorFlow with cuDNN acceleration on an NVIDIA GPU. Your dataset contains continuous numerical features with vastly different ranges.
What is the best way to standardize the data efficiently to improve model convergence?

A) Manually compute the feature mean and variance on the CPU and apply the transformation before training.
B) Apply batch normalization layers in the neural network to handle feature scaling dynamically during training.
C) Use cupy.linalg.norm() to normalize each feature vector individually to unit length.
D) Use cuml.StandardScaler() from RAPIDS to normalize the dataset before feeding it into the model.

5. You are working with cloud-based GPUs to process a large dataset (terabytes in size) stored in Parquet format. One column represents a unique identifier (e.g., product ID), and it contains only positive integers ranging from 1 to 100,000.
Which of the following data types provides the best balance of memory efficiency and performance?

A) float64
B) uint16
C) float32
D) int8

Solutions: