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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are training a deep learning model on a large dataset of images stored in an Amazon S3 bucket.
You want to optimize data loading, augmentation, and preprocessing on NVIDIA GPUs to avoid CPU bottlenecks.
Which of the following approaches is the most efficient for GPU-accelerated data preprocessing?

A) Load the dataset using PyTorch's torchvision.transforms and DataLoader, leveraging the CPU for data preprocessing and transferring batches to the GPU before training.
B) Use TensorFlow's tf.data API with tf.image transformations and ensure that the preprocessed images are transferred to GPU memory at the end of the pipeline.
C) Use OpenCV to load and preprocess images on the CPU, then transfer the processed images to the GPU before training.
D) Use NVIDIA DALI to decode images, apply transformations such as resizing and normalization, and load batches directly to the GPU for training.

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) Convert the dataset into a PyTorch tensor and use PyTorch's statistical methods
B) Use cuDF's built-in statistical functions like .mean(), .median(), and .var()
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. A data scientist is preprocessing a dataset containing several types of features:
A timestamp column storing millisecond-resolution timestamps.
A column with binary categorical values (Yes/No).
A column containing large continuous numerical values.
A column containing product category codes ranging from 0 to 5000.
Which of the following data type choices is the most optimal for maximizing GPU processing efficiency using NVIDIA cuDF?

A) Product category codes (range: 0-5000) fit within int16 (which can hold values from -32,768 to
32,767), making it more memory-efficient than int32.
B) Use float64 for timestamps, int8 for binary categorical values, float32 for continuous numerical values, and int32 for product category codes.
C) float32 is the best choice for large continuous numerical values, balancing precision and GPU efficiency.
D) Convert timestamps to datetime64[ms], encode binary values as bool, use float32 for continuous values, and int16 for product category codes.
E) Binary categorical values (Yes/No) should be stored as bool, which takes up minimal space.
F) Store timestamps as int64, encode binary values as float16, use float64 for continuous numerical values, and use int8 for product category codes.
G) Use string data type for timestamps, int32 for binary values, float16 for continuous numerical values, and int64 for product category codes.

4. You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
Which of the following is the best approach to achieve this goal?

A) Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively
B) Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib
C) Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly
D) Load the data into a Spark DataFrame and visualize using Apache Zeppelin

5. A machine learning engineer wants to evaluate the performance of NVIDIA RAPIDS cuDF and Apache Spark for large-scale data processing on a GPU-enabled cluster.
Which of the following strategies is the most effective for obtaining a fair and comprehensive benchmark?

A) Execute identical ETL workflows on cuDF and Spark-RAPIDS and measure execution time and resource utilization.
B) Focus only on processing speed without considering resource consumption differences between frameworks.
C) Limit the benchmark to small datasets since GPUs excel at parallel processing.
D) Run Spark on a CPU cluster while running RAPIDS on a GPU to compare real-world scenarios.

Solutions: