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  • NCP-ADS (NVIDIA-Certified-Professional Accelerated Data Science)
  • Exam Code: NCP-ADS
  • Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
  • Certification Provider: NVIDIA
  • Corresponding Certification:NVIDIA-Certified Professional
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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Analysis14%- Time-series analysis
  • 1. Anomaly detection in time-series datasets
  • 2. Time-series data handling and forecasting
- Visualization
  • 1. Visualizing data using Plotly and Matplotlib
  • 2. Selecting appropriate plots for different analysis goals
- Graph analytics
  • 1. Node importance evaluation and network relationship visualization
  • 2. Creating and analyzing graph data using cuGraph
- Exploratory data analysis
  • 1. Performing EDA on GPU-accelerated datasets
  • 2. Descriptive statistics and summary analysis
Topic 2: GPU and Cloud Computing16%- GPU resource management
  • 1. Efficient GPU resource allocation and scheduling
- GPU architecture and fundamentals
  • 1. GPU architecture fundamentals for data science
  • 2. CPU vs GPU workloads and memory transfer optimization
- Cloud GPU environments
  • 1. Cloud-based GPU instance configuration
  • 2. Containerized workflow deployment on cloud
- Performance optimization
  • 1. Memory profiling with DLProf
  • 2. Single and multi-GPU performance optimization
  • 3. Mixed precision and bottleneck analysis
Topic 3: MLOps19%- Experiment tracking
  • 1. Benchmarking workflows and selecting optimal hardware
  • 2. MLflow, Weights & Biases, and custom tracking tools
- Containerization and environment management
  • 1. Conda environment management
  • 2. Docker for reproducible GPU-accelerated workflows
- 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
Topic 4: Machine Learning15%- Feature engineering and hyperparameter tuning
  • 1. Batching and memory-efficient training methods
  • 2. Hyperparameter tuning techniques
  • 3. Feature engineering for ML models
- Model training with GPU acceleration
  • 1. Selection of appropriate algorithms for GPU execution
  • 2. Training models using cuML and GPU-accelerated XGBoost
  • 3. Multi-GPU training strategies
- Deep learning frameworks integration
  • 1. Overfitting vs underfitting concepts
  • 2. Using RAPIDS with TensorFlow and PyTorch
Topic 5: Data Manipulation and Software Literacy19%- Software literacy and development tools
  • 1. Python, NumPy, pandas, Jupyter proficiency
  • 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
- Distributed computing with Dask
  • 1. Dask-cuDF for parallel data processing
  • 2. Scaling data operations across multiple GPUs
- 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
Topic 6: Data Preparation17%- 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. 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

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

You are training a large-scale random forest model on a dataset with millions of rows and hundreds of features. The training time is significantly high when using traditional CPU-based machine learning frameworks.
Which NVIDIA technology should you use to accelerate training while maintaining compatibility with common ML frameworks like scikit-learn?

  • A. NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.
  • B. NVIDIA Triton Inference Server to distribute random forest model training across multiple GPUs.
  • C. NVIDIA TensorRT to accelerate random forest model training by optimizing tree-based algorithms.
  • D. NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.
Answer: A

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. Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.
  • B. Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
  • C. Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.
  • D. Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.
Answer: A

You are working on an AI-driven customer behavior prediction project.
According to the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, what is the most critical task to complete during the Data Understanding phase?

  • A. Deploying the model into a production environment for real-time inference.
  • B. Acquiring and exploring the dataset to assess quality, completeness, and potential biases.
  • C. Identifying and preparing feature engineering strategies to improve model accuracy.
  • D. Selecting the most appropriate machine learning algorithm for the prediction task.
Answer: B

You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

  • A. Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.
  • B. Using a single storage location for both raw and transformed data to simplify the workflow.
  • C. Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.
  • D. Ensuring the ETL pipeline uses only batch processing for data ingestion.
  • E. Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
Answer: A,C

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. cuML
  • B. Triton Inference Server
  • C. DLA (Deep Learning Accelerator)
  • D. cuBLAS
Answer: B

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