NCP-ADS Actual Braindumps & NCP-ADS Exam Guide Materials

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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Analysis14%- Visualization
  • 1. Visualizing data using Plotly and Matplotlib
  • 2. Selecting appropriate plots for different analysis goals
- Graph analytics
  • 1. Creating and analyzing graph data using cuGraph
  • 2. Node importance evaluation and network relationship visualization
- 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
Topic 2: GPU and Cloud Computing16%- GPU architecture and fundamentals
  • 1. CPU vs GPU workloads and memory transfer optimization
  • 2. GPU architecture fundamentals for data science
- 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
- Cloud GPU environments
  • 1. Containerized workflow deployment on cloud
  • 2. Cloud-based GPU instance configuration
Topic 3: MLOps19%- Model deployment and serving
  • 1. Production deployment strategies
  • 2. Model saving, loading, and prediction generation
- 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
- Model monitoring and management
  • 1. Monitoring production models for drift and performance degradation
  • 2. Managing model artifacts and configurations for reproducibility
Topic 4: Machine Learning15%- Feature engineering and hyperparameter tuning
  • 1. Feature engineering for ML models
  • 2. Batching and memory-efficient training methods
  • 3. Hyperparameter tuning techniques
- Deep learning frameworks integration
  • 1. Using RAPIDS with TensorFlow and PyTorch
  • 2. Overfitting vs underfitting concepts
- 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
Topic 5: Data Preparation17%- Feature engineering
  • 1. Dimensionality reduction and data sampling
  • 2. Feature engineering for numerical and categorical variables
- 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. Efficient processing and storage with Parquet
  • 2. RAPIDS-based ETL pipelines
- Data cleaning and quality handling
  • 1. Handling missing values and data quality issues
  • 2. Data governance and compliance
Topic 6: Data Manipulation and Software Literacy19%- 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. Data integration, joining, merging, and filtering
  • 2. cuDF vs pandas API mapping and usage
  • 3. Groupby, apply, and aggregation operations
- 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 (Q62-Q67):

NEW QUESTION # 62
A machine learning engineer is benchmarking a GPU-accelerated pipeline for data preprocessing and training. The pipeline consists of large-scale data transformations using RAPIDS cuDF and training a model with TensorFlow. The engineer wants to ensure that GPU utilization is maximized throughout the workflow.
Which approach is most effective in achieving this?

Answer: C


NEW QUESTION # 63
You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?

Answer: D


NEW QUESTION # 64
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 # 65
You are working with a social network dataset containing millions of user interactions and need to identify influential users based on their connectivity and interactions.
Which approach using NVIDIA's cuGraph library is the most appropriate for this task?

Answer: A


NEW QUESTION # 66
A data scientist needs to process a dataset containing 10 million records, performing transformations and exploratory data analysis (EDA). The processing needs to be efficient but does not require high- performance multi-GPU execution.
Which of the following libraries provides the best balance between usability and performance?

Answer: A


NEW QUESTION # 67
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