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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Preparation17%- GPU-accelerated ETL workflows
  • 1. RAPIDS-based ETL pipelines
  • 2. Efficient processing and storage with Parquet
- Feature engineering
  • 1. Feature engineering for numerical and categorical variables
  • 2. Dimensionality reduction and data sampling
- Data loading and preprocessing
  • 1. Handling class imbalance and generating synthetic data
  • 2. NVIDIA DALI for high-performance data loading
- Data cleaning and quality handling
  • 1. Handling missing values and data quality issues
  • 2. Data governance and compliance
Topic 2: MLOps19%- Containerization and environment management
  • 1. Docker for reproducible GPU-accelerated workflows
  • 2. Conda environment management
- 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. Production deployment strategies
  • 2. Model saving, loading, and prediction generation
- Experiment tracking
  • 1. Benchmarking workflows and selecting optimal hardware
  • 2. MLflow, Weights & Biases, and custom tracking tools
Topic 3: GPU and Cloud Computing16%- Cloud GPU environments
  • 1. Cloud-based GPU instance configuration
  • 2. Containerized workflow deployment on cloud
- Performance optimization
  • 1. Single and multi-GPU performance optimization
  • 2. Memory profiling with DLProf
  • 3. Mixed precision and bottleneck analysis
- 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
Topic 4: Data Analysis14%- 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
- Exploratory data analysis
  • 1. Performing EDA on GPU-accelerated datasets
  • 2. Descriptive statistics and summary analysis
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)
- 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. Dask-cuDF for parallel data processing
  • 2. Scaling data operations across multiple GPUs
Topic 6: Machine Learning15%- Deep learning frameworks integration
  • 1. Using RAPIDS with TensorFlow and PyTorch
  • 2. Overfitting vs underfitting concepts
- Feature engineering and hyperparameter tuning
  • 1. Feature engineering for ML models
  • 2. Batching and memory-efficient training methods
  • 3. Hyperparameter tuning techniques
- Model training with GPU acceleration
  • 1. Selection of appropriate algorithms for GPU execution
  • 2. Multi-GPU training strategies
  • 3. Training models using cuML and GPU-accelerated XGBoost

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q197-Q202):

NEW QUESTION # 197
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?

Answer: A


NEW QUESTION # 198
A data scientist is using NVIDIA RAPIDS to perform statistical analysis as part of exploratory data analysis (EDA) on a dataset containing millions of product reviews. They need to compute basic descriptive statistics such as mean, median, and variance efficiently.
Which of the following methods is the most appropriate for performing these calculations on GPUs?

Answer: D


NEW QUESTION # 199
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?

Answer: C


NEW QUESTION # 200
You are working on optimizing a deep learning model for inference on an NVIDIA GPU. You decide to use NVIDIA DLProf to profile the model and analyze its performance. After running DLProf, you review the generated reports and find that the GPU Utilization is significantly lower than expected.
Which of the following is the most likely reason for this issue, as indicated by the profiling data?

Answer: D


NEW QUESTION # 201
You are building a predictive model for retail sales forecasting and need a dataset that includes historical sales transactions, customer demographics, and external economic indicators (e.g., inflation rate, unemployment rate).
Which of the following datasets would be the most appropriate for your model?

Answer: C


NEW QUESTION # 202
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