NVIDIA - NCP-ADS - Useful Valid NVIDIA-Certified-Professional Accelerated Data Science Exam Review

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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Data Analysis14%- Time-series analysis
  • 1. Time-series data handling and forecasting
  • 2. Anomaly detection in time-series datasets
- Graph analytics
  • 1. Node importance evaluation and network relationship visualization
  • 2. Creating and analyzing graph data using cuGraph
- 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
Data Preparation17%- GPU-accelerated ETL workflows
  • 1. RAPIDS-based ETL pipelines
  • 2. Efficient processing and storage with Parquet
- Feature engineering
  • 1. Dimensionality reduction and data sampling
  • 2. Feature engineering for numerical and categorical variables
- Data cleaning and quality handling
  • 1. Data governance and compliance
  • 2. Handling missing values and data quality issues
- Data loading and preprocessing
  • 1. Handling class imbalance and generating synthetic data
  • 2. NVIDIA DALI for high-performance data loading
GPU and Cloud Computing16%- Performance optimization
  • 1. Mixed precision and bottleneck analysis
  • 2. Memory profiling with DLProf
  • 3. Single and multi-GPU performance optimization
- GPU resource management
  • 1. Efficient GPU resource allocation and scheduling
- Cloud GPU environments
  • 1. Containerized workflow deployment on cloud
  • 2. Cloud-based GPU instance configuration
- GPU architecture and fundamentals
  • 1. GPU architecture fundamentals for data science
  • 2. CPU vs GPU workloads and memory transfer optimization
Machine Learning15%- Deep learning frameworks integration
  • 1. Overfitting vs underfitting concepts
  • 2. Using RAPIDS with TensorFlow and PyTorch
- Model training with GPU acceleration
  • 1. Training models using cuML and GPU-accelerated XGBoost
  • 2. Multi-GPU training strategies
  • 3. Selection of appropriate algorithms for GPU execution
- Feature engineering and hyperparameter tuning
  • 1. Batching and memory-efficient training methods
  • 2. Feature engineering for ML models
  • 3. Hyperparameter tuning techniques
Data Manipulation and Software Literacy19%- Distributed computing with Dask
  • 1. Dask-cuDF for parallel data processing
  • 2. Scaling data operations across multiple GPUs
- 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. Python, NumPy, pandas, Jupyter proficiency
  • 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
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. Conda environment management
  • 2. Docker for reproducible GPU-accelerated workflows
- Model deployment and serving
  • 1. Production deployment strategies
  • 2. Model saving, loading, and prediction generation

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q281-Q286):

NEW QUESTION # 281
You are running a data science project on a cloud environment, where you need to optimize the GPU utilization for real-time data processing tasks.
Which of the following practices should you consider to maximize GPU performance? (Select two)

Answer: B,E


NEW QUESTION # 282
You are working with cuGraph to analyze a large social network dataset where users are represented as nodes, and their connections (friendships) are represented as edges. You need to determine the most efficient way to store and process the graph in cuGraph for high-performance analytics.
Which of the following graph representations is best suited for efficient processing in cuGraph?

Answer: C


NEW QUESTION # 283
Which of the following is the most efficient method for processing big data in a distributed environment using NVIDIA technologies?

Answer: D


NEW QUESTION # 284
Which of the following tools can be used for profiling deep learning models to identify performance bottlenecks and optimize execution on NVIDIA GPUs? (Select two)

Answer: B,C


NEW QUESTION # 285
You are working on a large-scale graph analysis project using NVIDIA cuGraph for accelerated computations. Your dataset consists of millions of nodes and edges representing social network interactions. You need to efficiently compute PageRank while minimizing memory usage.
Which of the following techniques would be the most effective?

Answer: C


NEW QUESTION # 286
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