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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. You are working on a machine learning pipeline using NVIDIA RAPIDS cuML and need to standardize the dataset to ensure that all features have a mean of 0 and a standard deviation of 1.
Which of the following methods should you use to achieve this in cuML?

A) cuml.preprocessing.StandardScaler()
B) cuml.preprocessing.MinMaxScaler()
C) cuml.preprocessing.LabelEncoder()
D) cuml.preprocessing.OneHotEncoder()

3. A data scientist is using RAPIDS cuML to build a predictive model on a large dataset containing numerical and categorical features.
To optimize feature engineering for accelerated GPU processing, which of the following is the best approach?

A) Normalize numerical features using min-max scaling implemented with cuDF, leveraging GPU acceleration.
B) Convert all categorical variables to string format for efficient processing in cuDF.
C) Apply one-hot encoding on high-cardinality categorical features directly in cuDF without any transformation.
D) Convert all numerical features to float64 to maximize precision for feature transformation.

4. 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) Use a traditional SQL database to compute statistics and then transfer results to the GPU
B) Use NumPy's statistical functions, such as numpy.mean() and numpy.var()
C) Convert the dataset into a PyTorch tensor and use PyTorch's statistical methods
D) Use cuDF's built-in statistical functions like .mean(), .median(), and .var()

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) float32
B) uint16
C) float64
D) int8

Solutions: