NCP-ADS Passleader Review - Vce NCP-ADS File

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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Machine Learning15%- 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. Batching and memory-efficient training methods
  • 2. Feature engineering for ML models
  • 3. Hyperparameter tuning techniques
- Deep learning frameworks integration
  • 1. Overfitting vs underfitting concepts
  • 2. Using RAPIDS with TensorFlow and PyTorch
Data Preparation17%- GPU-accelerated ETL workflows
  • 1. Efficient processing and storage with Parquet
  • 2. RAPIDS-based ETL pipelines
- 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
- Data cleaning and quality handling
  • 1. Handling missing values and data quality issues
  • 2. Data governance and compliance
Data Analysis14%- Time-series analysis
  • 1. Time-series data handling and forecasting
  • 2. Anomaly detection in time-series datasets
- 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
- Visualization
  • 1. Selecting appropriate plots for different analysis goals
  • 2. Visualizing data using Plotly and Matplotlib
MLOps19%- 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. Docker for reproducible GPU-accelerated workflows
  • 2. Conda environment management
- Experiment tracking
  • 1. MLflow, Weights & Biases, and custom tracking tools
  • 2. Benchmarking workflows and selecting optimal hardware
GPU and Cloud Computing16%- Cloud GPU environments
  • 1. Containerized workflow deployment on cloud
  • 2. Cloud-based GPU instance configuration
- Performance optimization
  • 1. Memory profiling with DLProf
  • 2. Single and multi-GPU performance optimization
  • 3. Mixed precision and bottleneck analysis
- 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
Data Manipulation and Software Literacy19%- 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
- 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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q223-Q228):

NEW QUESTION # 223
You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?

Answer: B


NEW QUESTION # 224
You are analyzing a large-scale transportation network using cuGraph and notice that query times are longer than expected when running graph algorithms.
What is the best way to optimize graph processing performance using GPU-accelerated tools?

Answer: C


NEW QUESTION # 225
You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?

Answer: D


NEW QUESTION # 226
A data science team is using NVIDIA GPUs to accelerate their AI workflow for a fraud detection system. They follow the CRISP-DM methodology to ensure a structured approach to the project.
During the Data Preparation phase, what is the most effective way to leverage NVIDIA technologies?

Answer: D


NEW QUESTION # 227
You are working on a large-scale social network analysis project using cuGraph. Your goal is to identify the most influential individuals in the network based on their connectivity and reach.
Which of the following cuGraph algorithms would be the most appropriate choice for this task?

Answer: D


NEW QUESTION # 228
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