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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q137-Q142):

NEW QUESTION # 137
You are tasked with acquiring a dataset for training a machine learning model in healthcare, predicting patient readmission rates. Before using the dataset, you must assess its quality.
Which of the following is the most important factor to evaluate before acquisition?

Answer: C


NEW QUESTION # 138
A data scientist is working with large-scale datasets in a RAPIDS AI pipeline and needs to efficiently process and organize the data while leveraging GPU acceleration.
Which of the following approaches best ensures optimized processing and memory management when using NVIDIA technologies?

Answer: A


NEW QUESTION # 139
Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?

Answer: B


NEW QUESTION # 140
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,D


NEW QUESTION # 141
You are tasked with cleansing a dataset containing numerical data that has significant outliers.
You're using pandas to identify and appropriately handle these outliers before applying CuDF for accelerated downstream analysis.
Which method effectively manages the numerical outliers while preserving the dataset's integrity for subsequent accelerated analytics?

Answer: B


NEW QUESTION # 142
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