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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: MLOps19%- Containerization and environment management
  • 1. Docker for reproducible GPU-accelerated workflows
  • 2. Conda environment management
- Model monitoring and management
  • 1. Managing model artifacts and configurations for reproducibility
  • 2. Monitoring production models for drift and performance degradation
- Model deployment and serving
  • 1. Model saving, loading, and prediction generation
  • 2. Production deployment strategies
- Experiment tracking
  • 1. Benchmarking workflows and selecting optimal hardware
  • 2. MLflow, Weights & Biases, and custom tracking tools
Topic 2: GPU and Cloud Computing16%- 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
- GPU resource management
  • 1. Efficient GPU resource allocation and scheduling
- Performance optimization
  • 1. Single and multi-GPU performance optimization
  • 2. Mixed precision and bottleneck analysis
  • 3. Memory profiling with DLProf
Topic 3: Data Analysis14%- 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
- Visualization
  • 1. Visualizing data using Plotly and Matplotlib
  • 2. Selecting appropriate plots for different analysis goals
- Time-series analysis
  • 1. Time-series data handling and forecasting
  • 2. Anomaly detection in time-series datasets
Topic 4: 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
- 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 5: Machine Learning15%- Model training with GPU acceleration
  • 1. Training models using cuML and GPU-accelerated XGBoost
  • 2. Selection of appropriate algorithms for GPU execution
  • 3. Multi-GPU training strategies
- Deep learning frameworks integration
  • 1. Overfitting vs underfitting concepts
  • 2. Using RAPIDS with TensorFlow and PyTorch
- 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%- Data cleaning and quality handling
  • 1. Data governance and compliance
  • 2. Handling missing values and data quality issues
- Feature engineering
  • 1. Feature engineering for numerical and categorical variables
  • 2. Dimensionality reduction and data sampling
- Data loading and preprocessing
  • 1. Handling class imbalance and generating synthetic data
  • 2. NVIDIA DALI for high-performance data loading
- GPU-accelerated ETL workflows
  • 1. RAPIDS-based ETL pipelines
  • 2. Efficient processing and storage with Parquet

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q176-Q181):

NEW QUESTION # 176
Which of the following tools in the NVIDIA AI stack is specifically designed to accelerate the deployment of machine learning models for production by optimizing inference performance?

Answer: B


NEW QUESTION # 177
You are using NVIDIA DLProf to analyze the performance of a deep learning model deployed on an A100 GPU. The report indicates that compute-bound operations are dominating execution time, and kernel execution efficiency is below 50%.
What is the best action to take based on this insight?

Answer: C


NEW QUESTION # 178
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: A


NEW QUESTION # 179
You are a data scientist analyzing a social media network with NVIDIA cuGraph to identify the most influential users using the PageRank algorithm.
Which option best describes how cuGraph PageRank operates on a directed graph?

Answer: A


NEW QUESTION # 180
In the context of the CRISP-DM process, you are tasked with building a machine learning model on a large dataset. You decide to leverage GPU resources to accelerate training.
Which of the following steps in the CRISP-DM process are most likely to benefit from the use of GPU resources in the cloud environment? (Select two)

Answer: D,E


NEW QUESTION # 181
......

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