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NVIDIA NCP-ADS Exam Syllabus Topics:
Section
Weight
Objectives
Topic 1: Data Analysis
14%
- Visualization
1. Visualizing data using Plotly and Matplotlib
2. Selecting appropriate plots for different analysis goals
- Exploratory data analysis
1. Performing EDA on GPU-accelerated datasets
2. Descriptive statistics and summary analysis
- Graph analytics
1. Creating and analyzing graph data using cuGraph
2. Node importance evaluation and network relationship visualization
- Time-series analysis
1. Anomaly detection in time-series datasets
2. Time-series data handling and forecasting
Topic 2: Machine Learning
15%
- Deep learning frameworks integration
1. Overfitting vs underfitting concepts
2. Using RAPIDS with TensorFlow and PyTorch
- 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. Hyperparameter tuning techniques
2. Batching and memory-efficient training methods
3. Feature engineering for ML models
Topic 3: GPU and Cloud Computing
16%
- 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
- 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
Topic 4: MLOps
19%
- Containerization and environment management
1. Conda environment management
2. Docker for reproducible GPU-accelerated workflows
- Experiment tracking
1. MLflow, Weights & Biases, and custom tracking tools
2. Benchmarking workflows and selecting optimal hardware
- Model deployment and serving
1. Production deployment strategies
2. Model saving, loading, and prediction generation
- Model monitoring and management
1. Monitoring production models for drift and performance degradation
2. Managing model artifacts and configurations for reproducibility
Topic 5: Data Preparation
17%
- GPU-accelerated ETL workflows
1. Efficient processing and storage with Parquet
2. RAPIDS-based ETL pipelines
- Feature engineering
1. Dimensionality reduction and data sampling
2. Feature engineering for numerical and categorical variables
- Data cleaning and quality handling
1. Data governance and compliance
2. Handling missing values and data quality issues
- Data loading and preprocessing
1. NVIDIA DALI for high-performance data loading
2. Handling class imbalance and generating synthetic data
Topic 6: Data Manipulation and Software Literacy
19%
- GPU-accelerated data manipulation using cuDF
1. Groupby, apply, and aggregation operations
2. cuDF vs pandas API mapping and usage
3. Data integration, joining, merging, and filtering
- 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
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are training a deep learning model on a large dataset and are deciding whether to use a single GPU or multiple GPUs. Which of the following are true considerations when comparing single-GPU and multi-GPU training setups? (Select two)
A. Multi-GPU setups perform better only when the batch size is reduced.
B. Single-GPU training is generally more cost-effective and should be preferred unless scaling is absolutely necessary.
C. Single-GPU training is limited by the VRAM (video memory) on the GPU, so larger models or datasets may require multi-GPU setups.
D. Multi-GPU training requires modifications to the model architecture to make it compatible with parallel processing.
E. Multi-GPU training can significantly reduce training time when the dataset is large and the model is computationally intensive.
You are working with a large dataset containing millions of high-resolution images for a deep learning project. The dataset needs to be processed efficiently on a GPU before training a model. Which NVIDIA technology is best suited for preprocessing, augmenting, and efficiently loading the dataset into memory?
A. NVIDIA Triton Inference Server to preprocess the dataset before model training.
B. NVIDIA Nsight Compute to optimize image dataset processing.
C. NVIDIA DALI (Data Loading Library) to accelerate data loading and preprocessing on the GPU.
D. NVIDIA RAPIDS cuDF to transform image data into tabular format for analysis.
A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries. Which of the following is the best approach for managing dependencies?
A. Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
B. Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.
C. Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.
D. Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.
A data scientist is analyzing a large time-series dataset containing stock price movements of thousands of companies over a decade. The dataset is stored as a cuDF DataFrame and contains millions of rows. The scientist wants to visualize trends and patterns interactively while leveraging GPU acceleration. Which of the following approaches is the most efficient for visualizing this time-series data?
A. Convert the cuDF DataFrame to Pandas and use matplotlib for plotting.
B. Use cuXfilter with a cuDF DataFrame to generate interactive visualizations directly on the GPU.
C. Use seaborn with a sampled subset of the dataset to generate line plots.
D. Use matplotlib with plt.plot() while applying df.to_pandas() to convert data.
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