NCP-ADS受験内容 & NCP-ADS受験対策解説集

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet - 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. Data governance and compliance 2. Handling missing values and data quality issues - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables
Topic 2: Machine Learning	15%	- 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 - Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Feature engineering for ML models 3. Batching and memory-efficient training methods - Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch
Topic 3: MLOps	19%	- Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - 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
Topic 4: GPU and Cloud Computing	16%	- GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science - 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 - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration
Topic 5: Data Manipulation and Software Literacy	19%	- 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 - GPU-accelerated data manipulation using cuDF 1. Data integration, joining, merging, and filtering 2. Groupby, apply, and aggregation operations 3. cuDF vs pandas API mapping and usage
Topic 6: Data Analysis	14%	- Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - 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 - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets

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NCP-ADS受験対策解説集、NCP-ADS試験勉強攻略

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NVIDIA-Certified-Professional Accelerated Data Science 認定 NCP-ADS 試験問題 (Q287-Q292):

質問 # 287
You are tasked with designing a benchmark to compare the performance of different GPU- accelerated machine learning frameworks, such as TensorFlow, PyTorch, and RAPIDS.
Which of the following factors is the most critical to ensure a fair and meaningful comparison?

A. Ensuring all frameworks use the same dataset, batch size, and GPU hardware during benchmarking.
B. Allowing each framework to run with its default settings, as tuning hyperparameters would bias the results.
C. Using the latest software versions of each framework, regardless of compatibility with the GPU hardware.
D. Using different model architectures per framework to test a variety of scenarios and ensure a broad evaluation.

正解：A

質問 # 288
You are building an MLOps pipeline for a predictive model that uses tabular data with both categorical and numerical features.
To ensure efficient data processing and optimal model training on an NVIDIA GPU, which of the following data types would be most suitable for a categorical feature representing different product categories?

A. String
B. Int64
C. Float64
D. Int32

正解：D

質問 # 289
In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A. Better for memory-intensive workloads
B. Lower energy consumption compared to CPUs
C. Lower cost of cloud infrastructure
D. Efficient handling of matrix operations in machine learning models
E. Faster parallel processing for large datasets

正解：D、E

質問 # 290
A data scientist is working with a large dataset that contains string-based numeric values that need to be converted to floating-point numbers for further analysis. The dataset is stored as a cuDF DataFrame, and the scientist needs to ensure the conversion is performed optimally on a GPU.
Which of the following is the best method for converting string-based numeric values to floating-point numbers using NVIDIA-accelerated processing?

A. Convert the cuDF DataFrame to a Pandas DataFrame first, then apply astype(float) and convert it back to cuDF.
B. Use pandas.to_numeric() since pandas automatically handles type conversion.
C. Use cudf.DataFrame.astype(float) to convert string values to floating-point numbers efficiently on a GPU.
D. Use NumPy's astype(float) method after converting the cuDF DataFrame into a NumPy array.

正解：C

質問 # 291
A financial institution is training a fraud detection model on a GPU-powered NVIDIA RAPIDS cuML pipeline. Their dataset includes customer ages, transaction amounts, merchant names, and transaction timestamps.
To optimize GPU memory usage while preserving accuracy, how should they store these features?

A. Convert all numeric data to float64 for maximum precision, as rounding errors in lower precision could impact the model's accuracy.
B. Keep customer ages in float16 format to reduce memory consumption, as integer types are less efficient in GPU operations.
C. Convert timestamps into UNIX epoch integers instead of using datetime64 for more efficient GPU computations.
D. Store transaction amounts as float32, customer ages as int8, merchant names as categorical, and timestamps as datetime64.

正解：D

質問 # 292
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