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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: MLOps | 19% | - End-to-end workflow management - Monitoring, logging and maintenance - Pipeline automation and orchestration - Model deployment and serving |
| Topic 2: Data Preparation | 17% | - Data validation and quality assurance - Feature engineering and data type optimization - Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation |
| Topic 3: Data Analysis | 14% | - Data visualization and graph analytics - Time-series analysis and anomaly detection - Distributed and parallel data processing - Exploratory Data Analysis (EDA) |
| Topic 4: Data Manipulation and Software Literacy | 19% | - Performance profiling and optimization tools - GPU-accelerated ETL workflows - Data processing libraries selection and usage - Dependency management and containerization |
| Topic 5: GPU and Cloud Computing | 16% | - CRISP-DM and data science methodology - GPU architecture and acceleration principles - Cloud GPU environments and deployment - Resource management and scaling strategies |
| Topic 6: Machine Learning | 15% | - Distributed training strategies - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning - Model evaluation and validation |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
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
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.
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").
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
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.
