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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a machine learning dataset with millions of rows and want to optimize performance on a GPU using CuDF. One of the features in your dataset represents categorical data with a limited number of unique values.
Which of the following data types should you use to maximize performance while reducing memory usage?

A) category
B) int64
C) bool
D) object (string)

2. You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.
Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?

A) df = cudf.to_gpu(pd.read_parquet("data.parquet"))
B) df = cudf.read_csv("data.parquet")
C) df = pd.read_parquet("data.parquet")
D) df = cudf.read_parquet("data.parquet")

3. In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A) Lower energy consumption compared to CPUs
B) Efficient handling of matrix operations in machine learning models
C) Faster parallel processing for large datasets
D) Better for memory-intensive workloads
E) Lower cost of cloud infrastructure

4. You are working with cuGraph to analyze a large social network dataset where users are represented as nodes, and their connections (friendships) are represented as edges. You need to determine the most efficient way to store and process the graph in cuGraph for high-performance analytics.
Which of the following graph representations is best suited for efficient processing in cuGraph?

A) Adjacency List representation using Python dictionaries
B) Edge List stored in a Pandas DataFrame
C) Compressed Sparse Row (CSR) format
D) Incidence Matrix representation

5. You are training a deep learning model for image classification and want to optimize its hyperparameters, including learning rate, batch size, and number of layers.
Which of the following techniques is the most effective for efficiently searching through a high- dimensional hyperparameter space?

A) Gradient Descent
B) Grid Search
C) Random Search
D) Bayesian Optimization

Solutions: