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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a large-scale machine learning project that requires preprocessing terabytes of structured and semi-structured data. You need a distributed data processing framework that can leverage NVIDIA GPUs efficiently to accelerate computations.
Which of the following approaches would best achieve this goal?

A) Using TensorFlow's Dataset API to load and preprocess the data on GPUs
B) Using plain NumPy with CUDA extensions to manually parallelize computations across multiple GPUs
C) Using Apache Spark with PySpark for CPU-based distributed data processing
D) Using Dask with RAPIDS cuDF and cuML for distributed GPU-accelerated processing

2. A data scientist is working with a dataset of sensor readings (temperature, pressure, vibration) in different scales and units. To ensure all features contribute equally to a machine learning model, the data needs to be standardized.
Which approach is best for standardizing numerical features?

A) Use Min-Max scaling to transform values into a fixed range (e.g., [0,1] or [-1,1]).
B) Apply z-score normalization (standardization) to scale values based on mean and standard deviation.
C) Apply log transformation to all numerical columns to force them into a uniform distribution.
D) Convert all numerical features to categorical values using binning.

3. You are working on a predictive maintenance model for industrial equipment. The dataset includes various sensor readings, categorical metadata, and timestamped events.
Which data type is the best choice for a feature representing the operating status of a machine, which has three possible states: "Idle", "Running", and "Error"?

A) Integer (0, 1, 2) to represent each state numerically.
B) Categorical encoding (one-hot encoding or ordinal encoding).
C) Floating-point representation (e.g., 0.1 for Idle, 0.5 for Running, 0.9 for Error).
D) Text (storing states as raw strings like "Idle", "Running", "Error").

4. A machine learning engineer is working with a financial dataset that contains multiple numerical features, including income, loan amount, and transaction frequency. Some features are normally distributed, while others have a highly skewed distribution with extreme outliers.
Which of the following approaches best ensures uniformity across features before training a model?

A) Use one-hot encoding to transform numerical features into categorical representations
B) Remove outliers before applying standardization
C) Scale all numerical features using min-max normalization
D) Apply log transformation to skewed features before standardizing them with z-score normalization

5. A data scientist is preprocessing a dataset containing multiple categorical features using NVIDIA RAPIDS to accelerate feature engineering.
The dataset contains:
A low-cardinality categorical feature (Product Type) with 10 unique values.
A high-cardinality categorical feature (User ID) with 100,000 unique values.
A numerical feature (Price) that requires transformation.
Which of the following feature engineering approaches will be the most efficient for GPU acceleration?

A) Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32.
B) Apply one-hot encoding to both Product Type and User ID, and scale Price using float64 precision.
C) Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency.
D) Frequency encoding for User ID is an efficient alternative to one-hot encoding, as it replaces each category with its frequency in the dataset, reducing dimensionality while preserving useful information.
E) Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price.
F) Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.

Solutions: