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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Data Preparation17%- Data loading and preprocessing
  • 1. NVIDIA DALI for high-performance data loading
  • 2. Handling class imbalance and generating synthetic data
- GPU-accelerated ETL workflows
  • 1. RAPIDS-based ETL pipelines
  • 2. Efficient processing and storage with Parquet
- Data cleaning and quality handling
  • 1. Data governance and compliance
  • 2. Handling missing values and data quality issues
- Feature engineering
  • 1. Dimensionality reduction and data sampling
  • 2. Feature engineering for numerical and categorical variables
Data Analysis14%- Visualization
  • 1. Visualizing data using Plotly and Matplotlib
  • 2. Selecting appropriate plots for different analysis goals
- Exploratory data analysis
  • 1. Performing EDA on GPU-accelerated datasets
  • 2. Descriptive statistics and summary analysis
- Graph analytics
  • 1. Node importance evaluation and network relationship visualization
  • 2. Creating and analyzing graph data using cuGraph
- Time-series analysis
  • 1. Time-series data handling and forecasting
  • 2. Anomaly detection in time-series datasets
Machine Learning15%- Feature engineering and hyperparameter tuning
  • 1. Feature engineering for ML models
  • 2. Batching and memory-efficient training methods
  • 3. Hyperparameter tuning techniques
- Model training with GPU acceleration
  • 1. Selection of appropriate algorithms for GPU execution
  • 2. Multi-GPU training strategies
  • 3. Training models using cuML and GPU-accelerated XGBoost
- Deep learning frameworks integration
  • 1. Using RAPIDS with TensorFlow and PyTorch
  • 2. Overfitting vs underfitting concepts
GPU and Cloud Computing16%- Performance optimization
  • 1. Memory profiling with DLProf
  • 2. Single and multi-GPU performance optimization
  • 3. Mixed precision and bottleneck analysis
- GPU architecture and fundamentals
  • 1. CPU vs GPU workloads and memory transfer optimization
  • 2. GPU architecture fundamentals for data science
- Cloud GPU environments
  • 1. Cloud-based GPU instance configuration
  • 2. Containerized workflow deployment on cloud
- GPU resource management
  • 1. Efficient GPU resource allocation and scheduling
Data Manipulation and Software Literacy19%- Distributed computing with Dask
  • 1. Dask-cuDF for parallel data processing
  • 2. Scaling data operations across multiple GPUs
- Software literacy and development tools
  • 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
  • 2. Python, NumPy, pandas, Jupyter proficiency
- 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
MLOps19%- Experiment tracking
  • 1. MLflow, Weights & Biases, and custom tracking tools
  • 2. Benchmarking workflows and selecting optimal hardware
- 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. Conda environment management
  • 2. Docker for reproducible GPU-accelerated workflows

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q83-Q88):

NEW QUESTION # 83
Which of the following methods are commonly used to handle missing data in data analysis? (Select two)

Answer: A,E


NEW QUESTION # 84
You are tasked with selecting the optimal data processing library for an AI project that involves handling varying dataset sizes. The project must be flexible enough to scale from small datasets (a few GBs) to large datasets (hundreds of GBs or more) using NVIDIA technologies.
Which of the following libraries would you choose for optimal performance at both small and large scales?

Answer: D


NEW QUESTION # 85
Your data science team is performing exploratory data analysis (EDA) on a large GPU-accelerated environment using cuDF and Dask-cuDF. During analysis, queries on categorical columns are performing poorly.
Which approach will most effectively improve query performance for categorical data in GPU-accelerated DataFrames?

Answer: A


NEW QUESTION # 86
You are working on a financial fraud detection system using NVIDIA RAPIDS cuML. You have a large time-series dataset of transaction amounts over time, and you need to identify anomalies such as unusual spikes in transactions.
Which of the following approaches is the most appropriate for detecting anomalies using NVIDIA technologies?

Answer: A


NEW QUESTION # 87
You need to deploy a containerized machine learning model that utilizes NVIDIA GPUs on a cloud- based Kubernetes cluster.
Which of the following steps is essential for ensuring proper GPU utilization inside a Docker container?

Answer: D


NEW QUESTION # 88
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