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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Machine Learning15%- Model training with GPU acceleration
  • 1. Selection of appropriate algorithms for GPU execution
  • 2. Training models using cuML and GPU-accelerated XGBoost
  • 3. Multi-GPU training strategies
- Feature engineering and hyperparameter tuning
  • 1. Hyperparameter tuning techniques
  • 2. Batching and memory-efficient training methods
  • 3. Feature engineering for ML models
- Deep learning frameworks integration
  • 1. Using RAPIDS with TensorFlow and PyTorch
  • 2. Overfitting vs underfitting concepts
Topic 2: Data Preparation17%- Data loading and preprocessing
  • 1. NVIDIA DALI for high-performance data loading
  • 2. Handling class imbalance and generating synthetic data
- Feature engineering
  • 1. Dimensionality reduction and data sampling
  • 2. Feature engineering for numerical and categorical variables
- GPU-accelerated ETL workflows
  • 1. Efficient processing and storage with Parquet
  • 2. RAPIDS-based ETL pipelines
- Data cleaning and quality handling
  • 1. Data governance and compliance
  • 2. Handling missing values and data quality issues
Topic 3: GPU and Cloud Computing16%- GPU architecture and fundamentals
  • 1. CPU vs GPU workloads and memory transfer optimization
  • 2. GPU architecture fundamentals for data science
- GPU resource management
  • 1. Efficient GPU resource allocation and scheduling
- Cloud GPU environments
  • 1. Cloud-based GPU instance configuration
  • 2. Containerized workflow deployment on cloud
- Performance optimization
  • 1. Memory profiling with DLProf
  • 2. Single and multi-GPU performance optimization
  • 3. Mixed precision and bottleneck analysis
Topic 4: MLOps19%- Model monitoring and management
  • 1. Monitoring production models for drift and performance degradation
  • 2. Managing model artifacts and configurations for reproducibility
- Experiment tracking
  • 1. MLflow, Weights & Biases, and custom tracking tools
  • 2. Benchmarking workflows and selecting optimal hardware
- Containerization and environment management
  • 1. Docker for reproducible GPU-accelerated workflows
  • 2. Conda environment management
- Model deployment and serving
  • 1. Model saving, loading, and prediction generation
  • 2. Production deployment strategies
Topic 5: Data Manipulation and Software Literacy19%- GPU-accelerated data manipulation using cuDF
  • 1. Data integration, joining, merging, and filtering
  • 2. cuDF vs pandas API mapping and usage
  • 3. Groupby, apply, and aggregation operations
- Distributed computing with Dask
  • 1. Scaling data operations across multiple GPUs
  • 2. Dask-cuDF for parallel data processing
- Software literacy and development tools
  • 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
  • 2. Python, NumPy, pandas, Jupyter proficiency
Topic 6: Data Analysis14%- Exploratory data analysis
  • 1. Performing EDA on GPU-accelerated datasets
  • 2. Descriptive statistics and summary analysis
- Graph analytics
  • 1. Creating and analyzing graph data using cuGraph
  • 2. Node importance evaluation and network relationship visualization
- Visualization
  • 1. Selecting appropriate plots for different analysis goals
  • 2. Visualizing data using Plotly and Matplotlib
- Time-series analysis
  • 1. Time-series data handling and forecasting
  • 2. Anomaly detection in time-series datasets

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q38-Q43):

NEW QUESTION # 38
Which of the following actions can you perform using DLProf to analyze a deep learning model's performance?

Answer: B


NEW QUESTION # 39
A data engineering team is designing an ETL pipeline to process large-scale financial transaction data. They want to leverage NVIDIA-accelerated ETL tools to extract data from a data lake, transform it by filtering and aggregating key fields, and load it into a data warehouse.
Which of the following approaches provides the most efficient ETL processing using NVIDIA technologies?

Answer: C


NEW QUESTION # 40
When deciding whether to use GPU acceleration or a traditional CPU approach for a machine learning task, which of the following factors should be considered to determine if the data qualifies as "big data" and whether GPU acceleration is beneficial? (Select two)

Answer: C,D


NEW QUESTION # 41
You are processing a dataset of high-resolution images for deep learning training and want to optimize image loading, augmentation, and transformation on an NVIDIA GPU.
Which of the following statements correctly describes the role of NVIDIA DALI (Data Loading Library) in accelerating data preparation?

Answer: C


NEW QUESTION # 42
You are working with a deep learning model for real-time inference on an NVIDIA GPU. Your objective is to optimize inference speed while maintaining acceptable model accuracy.
Which of the following techniques provides the best balance between inference performance and accuracy?

Answer: D


NEW QUESTION # 43
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