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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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The NVIDIA NCP-ADS certification exam is a valuable credential that often comes with certain personal and professional benefits. For many NVIDIA professionals, the NVIDIA-Certified-Professional Accelerated Data Science (NCP-ADS) certification exam is not just a valuable way to boost their skills but also NVIDIA-Certified-Professional Accelerated Data Science certification exam gives them an edge in the job market or the corporate ladder. There are other several advantages that successful NVIDIA NCP-ADS Exam candidates can gain after passing the NVIDIA NCP-ADS exam.

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q229-Q234):

NEW QUESTION # 229
You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

Answer: A


NEW QUESTION # 230
You are working on a structured dataset of around 10GB and need to perform exploratory data analysis (EDA), feature engineering, and filtering operations efficiently using NVIDIA technologies. The dataset fits into a single GPU's memory.
Which data processing library should you use to achieve the best performance?

Answer: A


NEW QUESTION # 231
Which of the following Nvidia technologies is commonly used for deploying machine learning models in production environments, enabling scalable deployment and monitoring?

Answer: A


NEW QUESTION # 232
You need to set up an isolated, GPU-accelerated environment for a deep learning project that requires specific CUDA, cuDNN, and RAPIDS versions.
Which of the following best ensures a reproducible environment using Docker?

Answer: B


NEW QUESTION # 233
You are working with a dataset containing billions of records stored in a Parquet file. You need to load this dataset efficiently into an NVIDIA-accelerated RAPIDS environment for feature engineering.
Which of the following is the best approach?

Answer: A


NEW QUESTION # 234
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