NCP-ADS復習教材 & NCP-ADS参考資料

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: GPU and Cloud Computing	16%	- Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - Performance optimization 1. Mixed precision and bottleneck analysis 2. Single and multi-GPU performance optimization 3. Memory profiling with DLProf - 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
Topic 2: Machine Learning	15%	- 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 - 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
Topic 3: Data Preparation	17%	- Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - 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
Topic 4: Data Analysis	14%	- Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib - 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
Topic 5: MLOps	19%	- 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. Model saving, loading, and prediction generation 2. Production deployment strategies - Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware - Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows
Topic 6: Data Manipulation and Software Literacy	19%	- GPU-accelerated data manipulation using cuDF 1. cuDF vs pandas API mapping and usage 2. Groupby, apply, and aggregation operations 3. Data integration, joining, merging, and filtering - 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

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NVIDIA-Certified-Professional Accelerated Data Science 認定 NCP-ADS 試験問題 (Q84-Q89):

質問 # 84
You are working with a large dataset containing millions of rows, and you need to store it efficiently for fast read and write operations while maintaining compatibility with CuDF and pandas.
Which of the following file formats is the best choice for efficient columnar storage and GPU-accelerated processing?

A. CSV
B. JSON
C. TXT
D. Parquet

正解：D

質問 # 85
You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
Which of the following is the best approach to achieve this goal?

A. Load the data into a Spark DataFrame and visualize using Apache Zeppelin
B. Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib
C. Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively
D. Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly

正解：C

質問 # 86
A data scientist is working with large-scale ETL (Extract, Transform, Load) pipelines on GPU- accelerated infrastructure using RAPIDS. The workload involves frequent shuffle operations, which significantly impact performance.
What is the best approach using NVIDIA technologies to reduce shuffle overhead and improve performance?

A. Store all intermediate shuffle data in CPU memory using pandas to ensure persistence and reduce GPU load.
B. Use RAPIDS cuDF's GPU memory caching to store intermediate DataFrames and avoid redundant shuffle operations.
C. Use RAPIDS cuML to replace shuffle-intensive operations with an ML model that predicts data distribution.
D. Enable CUDA Unified Memory to automatically optimize shuffle performance without manual intervention.

正解：B

質問 # 87
When scaling a distributed data processing framework using NVIDIA GPU technology for big data processing, which of the following factors is most critical to optimize performance?

A. Maximizing the amount of data transferred between GPUs for faster processing.
B. Using more CPU cores to handle computation-heavy tasks.
C. Limiting the number of GPU nodes used in the cluster to avoid complexity.
D. Ensuring the proper configuration of GPU resources across all nodes in the distributed system.

正解：D

質問 # 88
A data science team wants to deploy a GPU-accelerated pipeline using cuGraph to analyze graph data on cloud infrastructure. They are evaluating different cloud-based GPU solutions.
Which of the following factors should they consider when selecting a cloud-based GPU instance for running cuGraph efficiently?

A. The availability of NVIDIA CUDA-enabled GPUs, as cuGraph requires CUDA for acceleration.
B. Cloud-based GPUs are only useful for rendering graphics, not for running cuGraph algorithms.
C. cuGraph runs equally well on CPU-based virtual machines, making GPU instances unnecessary.
D. The choice of GPU instance does not affect cuGraph performance since all GPUs execute graph algorithms at the same speed.

正解：A

質問 # 89
......

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