NCP-ADS難易度、NCP-ADS合格受験記

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables
Topic 2: MLOps	19%	- Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation - Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools
Topic 3: Data Manipulation and Software Literacy	19%	- 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 - Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency
Topic 4: Machine Learning	15%	- 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. Multi-GPU training strategies 2. Training models using cuML and GPU-accelerated XGBoost 3. Selection of appropriate algorithms for GPU execution
Topic 5: Data Analysis	14%	- Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets
Topic 6: GPU and Cloud Computing	16%	- GPU architecture and fundamentals 1. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - Performance optimization 1. Single and multi-GPU performance optimization 2. Mixed precision and bottleneck analysis 3. Memory profiling with DLProf - GPU resource management 1. Efficient GPU resource allocation and scheduling

>> NCP-ADS難易度 <<

NVIDIA NCP-ADS合格受験記 & NCP-ADS試験過去問

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NVIDIA-Certified-Professional Accelerated Data Science 認定 NCP-ADS 試験問題 (Q235-Q240):

質問 # 235
You are working on a financial dataset that tracks stock prices over time, and you need to detect anomalies such as sudden spikes or drops using NVIDIA technologies.
Which of the following approaches would be the most effective for anomaly detection in a time-series dataset using NVIDIA's RAPIDS AI and TensorRT?

A. Apply a traditional rule-based thresholding method using pandas and NumPy for detecting sudden spikes in stock prices.
B. Perform anomaly detection by applying DBSCAN clustering with RAPIDS cuML without any feature engineering.
C. Use traditional ARIMA modeling with RAPIDS cuML to classify anomalies based on residual analysis.
D. Use RAPIDS cuML's Isolation Forest for anomaly detection and deploy it with NVIDIA Triton Inference Server.

正解：D

質問 # 236
A data scientist is analyzing a large dataset of financial transactions containing millions of records.
To efficiently perform exploratory data analysis (EDA) using RAPIDS cuDF, which approach provides the most optimized performance while ensuring comprehensive insights?

A. Use RAPIDS cuDF functions like .describe() and .value_counts() to perform statistical summaries directly on the GPU.
B. Perform all analysis on the CPU to avoid potential GPU memory limitations.
C. Convert the dataset to a Pandas DataFrame for easier visualization and use .describe() to summarize statistics.
D. Downsample the dataset and analyze a subset using Pandas for efficiency.

正解：A

質問 # 237
Which of the following tools in the NVIDIA AI stack is specifically designed to accelerate the deployment of machine learning models for production by optimizing inference performance?

A. DLA (Deep Learning Accelerator)
B. cuML
C. Triton Inference Server
D. cuBLAS

正解：C

質問 # 238
You are a data scientist analyzing a social media network with NVIDIA cuGraph to identify the most influential users using the PageRank algorithm.
Which option best describes how cuGraph PageRank operates on a directed graph?

A. PageRank assigns equal importance to all nodes in the graph initially and updates values only based on outgoing edges, ignoring incoming edges.
B. PageRank in cuGraph is a label propagation algorithm that clusters nodes into communities rather than ranking their importance.
C. PageRank in cuGraph uses an iterative power method to update node importance values based on incoming edges, incorporating a damping factor to handle random jumps.
D. PageRank in cuGraph operates only on undirected graphs and cannot be applied to networks where edges have a direction.

正解：C

質問 # 239
You have a massive time-series dataset containing millions of records per day, and you need to perform forecasting at scale.
Which of the following techniques best utilizes NVIDIA technologies to optimize time-series forecasting?

A. Use RAPIDS cuML's XGBoost-GPU implementation to forecast time-series patterns with improved training efficiency.
B. Use RAPIDS cuDF and Dask-cuDF to preprocess the data and train an LSTM model with PyTorch Lightning on multiple GPUs.
C. Run Facebook Prophet on a multi-core CPU environment to leverage parallel forecasting.
D. Convert time-series data to tensors and train a deep learning model using PyTorch with mixed precision on an NVIDIA GPU.

正解：B

質問 # 240
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