NCP-ADS模擬練習 & 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%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. Benchmarking GPU workflows 3. CRISP-DM workflow execution
Topic 2: MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Performance benchmarking and optimization 3. Model deployment in production environments
Topic 3: Machine Learning	15%	- Model Development and Optimization 1. Multi-GPU training comparison 2. Feature engineering 3. Hyperparameter tuning 4. Memory optimization techniques (mixed precision, batching)
Topic 4: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Detect anomalies in time series datasets 2. Perform time series analysis and visualization 3. Use cuGraph for graph analytics
Topic 5: Data Preparation	17%	- Data Cleaning and Transformation 1. cuDF and pandas data preprocessing 2. Synthetic data generation with RAPIDS 3. Data normalization and standardization
Topic 6: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. GPU-accelerated ETL design and implementation 2. Distributed data processing frameworks (Dask) 3. Data caching and performance optimization

>> NCP-ADS模擬練習 <<

NVIDIA NCP-ADS参考資料、NCP-ADS模擬体験

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NVIDIA-Certified-Professional Accelerated Data Science 認定 NCP-ADS 試験問題 (Q12-Q17):

質問 # 12
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?

A. Use cuML's StandardScaler() to transform the features to have zero mean and unit variance.
B. Apply cuDF's normalize() function to scale each feature between 0 and 1.
C. Apply cuML's RobustScaler() to center the data using median and scale using the interquartile range.
D. Use cuML's PCA to directly remove the need for standardization by reducing dimensionality.

正解：A

質問 # 13
You are working with a large-scale social network dataset and need to analyze relationships between users to detect communities using the Louvain algorithm. Given the benefits of GPU acceleration, you decide to use cuGraph for this task.
Which of the following statements best describes why cuGraph is beneficial for this workload?

A. cuGraph does not support weighted graphs, making it less useful for real-world social network analysis.
B. Running graph algorithms on a GPU leads to higher latency due to the overhead of data movement between CPU and GPU.
C. cuGraph enables parallel computation on the GPU, significantly speeding up community detection tasks compared to traditional CPU-based implementations.
D. cuGraph primarily accelerates deep learning tasks and is not optimized for graph-based algorithms.

正解：C

質問 # 14
A data scientist is working with a large dataset containing millions of records and aims to accelerate the data preprocessing workflow using NVIDIA technologies.
Which of the following approaches is the most effective for optimizing data preprocessing performance using GPUs?

A. Using Dask to distribute processing across multiple CPU cores
B. Running data transformations in a multi-threaded CPU environment
C. Using NumPy for array computations and parallelizing with Python's multiprocessing
D. Using RAPIDS cuDF to replace pandas operations

正解：D

質問 # 15
You are working on a dataset containing missing values, duplicate records, and inconsistent data types.
The dataset size is 15GB and you need to efficiently perform data cleansing operations such as:
- Handling missing values
- Dropping duplicates
- Converting data types
Which of the following approaches would be the most efficient way to perform these operations on an NVIDIA GPU?

A. Use pandas to load the dataset and perform the operations using standard pandas functions
B. Convert the dataset into a NumPy array and process it using CuPy before converting it back to a DataFrame
C. Use Vaex to perform data cleansing, as it is optimized for large-scale datasets
D. Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods

正解：D

質問 # 16
You are deploying an NVIDIA GPU-accelerated machine learning model in a Docker container and want to ensure that your application can leverage the GPU efficiently.
What is the best way to manage CUDA dependencies and avoid compatibility issues inside your Docker container?

A. Use a base Ubuntu image and install TensorFlow, PyTorch, and CUDA using pip install inside the container.
B. Manually install CUDA and cuDNN inside the container by downloading them from NVIDIA's website and setting environment variables.
C. Disable GPU acceleration in Docker and force computations on the CPU to avoid CUDA compatibility issues.
D. Use NVIDIA's official Docker images from NVIDIA GPU Cloud (NGC), which come with pre-installed CUDA and AI frameworks.

正解：D

質問 # 17
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