NCP-ADS합격보장가능시험대비자료최신덤프공부자료

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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: MLOps19%- Model deployment and serving
  • 1. Model saving, loading, and prediction generation
  • 2. Production deployment strategies
- Containerization and environment management
  • 1. Docker for reproducible GPU-accelerated workflows
  • 2. Conda environment management
- Model monitoring and management
  • 1. Monitoring production models for drift and performance degradation
  • 2. Managing model artifacts and configurations for reproducibility
- Experiment tracking
  • 1. Benchmarking workflows and selecting optimal hardware
  • 2. MLflow, Weights & Biases, and custom tracking tools
Topic 2: Machine Learning15%- Feature engineering and hyperparameter tuning
  • 1. Hyperparameter tuning techniques
  • 2. Batching and memory-efficient training methods
  • 3. Feature engineering for ML models
- 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. Multi-GPU training strategies
  • 3. Selection of appropriate algorithms for GPU execution
Topic 3: GPU and Cloud Computing16%- 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. Cloud-based GPU instance configuration
  • 2. Containerized workflow deployment on cloud
- GPU resource management
  • 1. Efficient GPU resource allocation and scheduling
Topic 4: Data Manipulation and Software Literacy19%- GPU-accelerated data manipulation using cuDF
  • 1. cuDF vs pandas API mapping and usage
  • 2. Data integration, joining, merging, and filtering
  • 3. Groupby, apply, and aggregation operations
- Distributed computing with Dask
  • 1. Scaling data operations across multiple GPUs
  • 2. Dask-cuDF for parallel data processing
- Software literacy and development tools
  • 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
  • 2. Python, NumPy, pandas, Jupyter proficiency
Topic 5: Data Analysis14%- Visualization
  • 1. Selecting appropriate plots for different analysis goals
  • 2. Visualizing data using Plotly and Matplotlib
- Exploratory data analysis
  • 1. Performing EDA on GPU-accelerated datasets
  • 2. Descriptive statistics and summary analysis
- Time-series analysis
  • 1. Anomaly detection in time-series datasets
  • 2. Time-series data handling and forecasting
- Graph analytics
  • 1. Node importance evaluation and network relationship visualization
  • 2. Creating and analyzing graph data using cuGraph
Topic 6: Data Preparation17%- 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
- GPU-accelerated ETL workflows
  • 1. RAPIDS-based ETL pipelines
  • 2. Efficient processing and storage with Parquet
- Feature engineering
  • 1. Dimensionality reduction and data sampling
  • 2. Feature engineering for numerical and categorical variables

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최신 NVIDIA-Certified Professional NCP-ADS 무료샘플문제 (Q234-Q239):

질문 # 234
A data scientist is working on a dataset where the numerical features have different ranges, and they need to ensure uniformity across features before training a machine learning model.
Which of the following approaches, utilizing NVIDIA technologies, would best achieve this goal?

정답：C


질문 # 235
In a typical MLOps pipeline, which of the following practices are essential to ensuring robust deployment and monitoring of machine learning models in production? (Select two)

정답：B,D


질문 # 236
You are working on an MLOps workflow that loads a dataset into GPU memory for model training using RAPIDS cuDF. Before performing transformations, you want to verify that the dataset will fit into available GPU memory.
Which of the following methods provides the most accurate estimate of dataset memory consumption in a RAPIDS cudf.DataFrame?

정답：C


질문 # 237
A data scientist is training a deep learning model and wants to find the best learning rate to optimize convergence speed and generalization. The scientist tests different values: A very small learning rate (0.00001) results in slow convergence.
A very large learning rate (10) causes the model loss to fluctuate wildly and not converge.
Which of the following strategies is the most effective way to optimize the learning rate dynamically during training?

정답：A


질문 # 238
In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

정답：C,E


질문 # 239
......

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