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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Machine Learning15%- Deep learning frameworks integration
  • 1. Overfitting vs underfitting concepts
  • 2. Using RAPIDS with TensorFlow and PyTorch
- Feature engineering and hyperparameter tuning
  • 1. Hyperparameter tuning techniques
  • 2. Feature engineering for ML models
  • 3. Batching and memory-efficient training methods
- 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
MLOps19%- Model monitoring and management
  • 1. Managing model artifacts and configurations for reproducibility
  • 2. Monitoring production models for drift and performance degradation
- Experiment tracking
  • 1. Benchmarking workflows and selecting optimal hardware
  • 2. MLflow, Weights & Biases, and custom tracking tools
- 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
Data Manipulation and Software Literacy19%- GPU-accelerated data manipulation using cuDF
  • 1. Groupby, apply, and aggregation operations
  • 2. Data integration, joining, merging, and filtering
  • 3. cuDF vs pandas API mapping and usage
- Software literacy and development tools
  • 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
  • 2. Python, NumPy, pandas, Jupyter proficiency
- Distributed computing with Dask
  • 1. Scaling data operations across multiple GPUs
  • 2. Dask-cuDF for parallel data processing
Data Preparation17%- Data cleaning and quality handling
  • 1. Data governance and compliance
  • 2. Handling missing values and data quality issues
- GPU-accelerated ETL workflows
  • 1. RAPIDS-based ETL pipelines
  • 2. Efficient processing and storage with Parquet
- Data loading and preprocessing
  • 1. NVIDIA DALI for high-performance data loading
  • 2. Handling class imbalance and generating synthetic data
- Feature engineering
  • 1. Feature engineering for numerical and categorical variables
  • 2. Dimensionality reduction and data sampling
Data Analysis14%- Exploratory data analysis
  • 1. Descriptive statistics and summary analysis
  • 2. Performing EDA on GPU-accelerated datasets
- 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
- Graph analytics
  • 1. Node importance evaluation and network relationship visualization
  • 2. Creating and analyzing graph data using cuGraph
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 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. CPU vs GPU workloads and memory transfer optimization
  • 2. GPU architecture fundamentals for data science

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q67-Q72):

NEW QUESTION # 67
You are comparing the performance of NVIDIA RAPIDS cuML, TensorFlow, and PyTorch for training and inference on a dataset with millions of records.
To design a fair and effective benchmark, which approach should you take?

Answer: B


NEW QUESTION # 68
A machine learning engineer is tasked with optimizing an image classification model on a cloud platform. The engineer must select a GPU-accelerated instance that balances cost and performance while ensuring compatibility with frameworks like TensorFlow and PyTorch.
Which instance configuration is the most appropriate choice?

Answer: B


NEW QUESTION # 69
You are working on an MLOps pipeline with a large dataset using NVIDIA technologies. You need to assess the memory size of the dataset before training the model to avoid memory overflow errors.
Which of the following is the best method for verifying the memory size of the dataset on the GPU using NVIDIA tools?

Answer: A


NEW QUESTION # 70
When utilizing GPU instances in a cloud environment for data science, which of the following are common considerations? (Select two)

Answer: B,D


NEW QUESTION # 71
You are working with a large dataset containing millions of high-resolution images for a deep learning project. The dataset needs to be processed efficiently on a GPU before training a model.
Which NVIDIA technology is best suited for preprocessing, augmenting, and efficiently loading the dataset into memory?

Answer: A


NEW QUESTION # 72
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