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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. Batching and memory-efficient training methods
  • 3. Feature engineering for ML models
- 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
Data Analysis14%- Graph analytics
  • 1. Node importance evaluation and network relationship visualization
  • 2. Creating and analyzing graph data using cuGraph
- Exploratory data analysis
  • 1. Performing EDA on GPU-accelerated datasets
  • 2. Descriptive statistics and summary analysis
- Time-series analysis
  • 1. Time-series data handling and forecasting
  • 2. Anomaly detection in time-series datasets
- Visualization
  • 1. Visualizing data using Plotly and Matplotlib
  • 2. Selecting appropriate plots for different analysis goals
GPU and Cloud Computing16%- GPU resource management
  • 1. Efficient GPU resource allocation and scheduling
- GPU architecture and fundamentals
  • 1. CPU vs GPU workloads and memory transfer optimization
  • 2. GPU architecture fundamentals for data science
- Performance optimization
  • 1. Single and multi-GPU performance optimization
  • 2. Mixed precision and bottleneck analysis
  • 3. Memory profiling with DLProf
- Cloud GPU environments
  • 1. Containerized workflow deployment on cloud
  • 2. Cloud-based GPU instance configuration
MLOps19%- Experiment tracking
  • 1. Benchmarking workflows and selecting optimal hardware
  • 2. MLflow, Weights & Biases, and custom tracking tools
- 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. Production deployment strategies
  • 2. Model saving, loading, and prediction generation
- Containerization and environment management
  • 1. Conda environment management
  • 2. Docker for reproducible GPU-accelerated workflows
Data Preparation17%- 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
- Data cleaning and quality handling
  • 1. Handling missing values and data quality issues
  • 2. Data governance and compliance
- Feature engineering
  • 1. Dimensionality reduction and data sampling
  • 2. Feature engineering for numerical and categorical variables
Data Manipulation and Software Literacy19%- Software literacy and development tools
  • 1. Python, NumPy, pandas, Jupyter proficiency
  • 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
- 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
- Distributed computing with Dask
  • 1. Dask-cuDF for parallel data processing
  • 2. Scaling data operations across multiple GPUs

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q287-Q292):

NEW QUESTION # 287
You are working with a large dataset in a cloud environment for a deep learning model. The dataset consists of several features including numerical values, categorical data, and timestamps.
Which of the following choices would result in the most efficient use of GPU and cloud resources when determining the optimal data type for each feature? (Select three)

Answer: C,D,E


NEW QUESTION # 288
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: C


NEW QUESTION # 289
Which of the following methods are commonly used to handle missing data in data analysis? (Select two)

Answer: C,D


NEW QUESTION # 290
A data engineering team is tasked with processing terabytes of log data every hour using an ETL pipeline. Due to the large data volume, they need a scalable GPU-accelerated solution that can distribute data processing across multiple GPUs.
Which approach best meets their needs?

Answer: D


NEW QUESTION # 291
A machine learning team needs to process terabytes of image metadata stored in a distributed storage system. They want to leverage GPU acceleration to speed up preprocessing and transformation while ensuring efficient parallel access.
Which of the following approaches best aligns with NVIDIA's accelerated data science ecosystem?

Answer: C


NEW QUESTION # 292
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