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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Manipulation and Software Literacy	19%	- 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 - GPU-accelerated data manipulation using cuDF 1. Groupby, apply, and aggregation operations 2. Data integration, joining, merging, and filtering 3. cuDF vs pandas API mapping and usage
Topic 2: Machine Learning	15%	- Model training with GPU acceleration 1. Multi-GPU training strategies 2. Selection of appropriate algorithms for GPU execution 3. Training models using cuML and GPU-accelerated XGBoost - Feature engineering and hyperparameter tuning 1. Feature engineering for ML models 2. Hyperparameter tuning techniques 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: Data Preparation	17%	- Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - 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 - GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet
Topic 4: GPU and Cloud Computing	16%	- Performance optimization 1. Memory profiling with DLProf 2. Single and multi-GPU performance optimization 3. Mixed precision and bottleneck analysis - GPU resource management 1. Efficient GPU resource allocation and scheduling - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science
Topic 5: MLOps	19%	- 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 - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management
Topic 6: Data Analysis	14%	- 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. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q263-Q268):

NEW QUESTION # 263
You are working on a large dataset for a machine learning model and need to preprocess the data efficiently using NVIDIA RAPIDS cuDF on a GPU-accelerated system.
Which of the following statements is correct regarding data preparation using cuDF?

A. cuDF operations are executed lazily and require an explicit compute() call to execute transformations.
B. cuDF supports common Pandas-like operations, such as filtering, merging, and groupby, but performs them efficiently on the GPU.
C. cuDF can be used as a direct replacement for Pandas, but it is optimized for multi-node distributed computing rather than GPU acceleration.
D. cuDF requires all input data to be stored in Apache Parquet format, as it does not support CSV or JSON ingestion.

Answer: B

NEW QUESTION # 264
You are using RAPIDS and Dask-cuDF to process a large-scale ETL pipeline. The workflow involves multiple join and groupby operations, which are causing excessive shuffling.
How can you best optimize caching to reduce shuffle overhead?

A. Use dask.persist() to store frequently accessed cuDF DataFrames in GPU memory, reducing recomputation and shuffle operations.
B. Split the dataset into multiple smaller Pandas DataFrames and store them in memory for quick retrieval.
C. Cache data using Apache Arrow's in-memory format, but process all operations on CPU.
D. Force every operation to be recomputed from the raw dataset to ensure accurate results.

Answer: A

NEW QUESTION # 265
A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

A. Using a single-threaded feature extraction approach to avoid overhead from parallelization.
B. Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.
C. Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.
D. Reducing dataset size by randomly removing data points without considering class balance.

Answer: B

NEW QUESTION # 266
You are setting up a GPU-accelerated data science environment on a cloud-based instance that utilizes NVIDIA GPUs.
To ensure compatibility between CUDA, RAPIDS, and Python libraries, which of the following is the most effective approach for managing dependencies and avoiding version conflicts?

A. Install RAPIDS, PyTorch, TensorFlow, and other libraries in the same Conda base environment for simplified access
B. Use Conda environments with version-pinned dependencies to create an isolated environment for RAPIDS and CUDA
C. Manually install required libraries in a system-wide Python environment using pip install without version specifications
D. Use a virtual machine with all dependencies pre-installed to avoid any software conflicts

Answer: B

NEW QUESTION # 267
A data scientist is working on a customer segmentation model using NVIDIA RAPIDS on GPUs. The dataset contains millions of customer records with features such as transaction history, age, location, and frequency of visits.
To optimize feature engineering using NVIDIA technologies, what is the best approach?

A. Perform feature engineering on CPUs using pandas before transferring data to the GPU for training.
B. Convert all categorical variables into string representations to preserve their original format for later analysis.
C. Use cuDF to perform feature transformations like normalization and one-hot encoding directly on the GPU.
D. Store all numerical features in float64 format to prevent rounding errors during transformations.

Answer: C

NEW QUESTION # 268
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