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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- 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 - Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph
Topic 2: Data Preparation	17%	- Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - 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 - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines
Topic 3: MLOps	19%	- 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 - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility
Topic 4: GPU and Cloud Computing	16%	- GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science - 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
Topic 5: Machine Learning	15%	- Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts - Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Feature engineering for ML models 3. Batching and memory-efficient training methods - Model training with GPU acceleration 1. Multi-GPU training strategies 2. Training models using cuML and GPU-accelerated XGBoost 3. Selection of appropriate algorithms for GPU execution
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 - Software literacy and development tools 1. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) - Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q10-Q15):

NEW QUESTION # 10
You are working with cuGraph to analyze a large social network dataset where users are represented as nodes, and their connections (friendships) are represented as edges. You need to determine the most efficient way to store and process the graph in cuGraph for high-performance analytics.
Which of the following graph representations is best suited for efficient processing in cuGraph?

A. Incidence Matrix representation
B. Edge List stored in a Pandas DataFrame
C. Compressed Sparse Row (CSR) format
D. Adjacency List representation using Python dictionaries

Answer: C

NEW QUESTION # 11
A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?

A. Manually installing each package and its dependencies using pip
B. Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies
C. Using only virtual environments (venv) without managing GPU dependencies separately
D. Using a single system-wide installation of CUDA and forcing all projects to use the same version

Answer: B

NEW QUESTION # 12
You are working with a data science project that requires GPU acceleration for machine learning tasks. Your team is facing challenges with software version conflicts between different dependencies when deploying the project on different systems.
Which of the following solutions should you consider to efficiently manage software dependencies and avoid conflicts? (Select two)

A. Set up a virtual machine for each different dependency configuration to isolate environments.
B. Install GPU drivers on the host machine and rely on the local system environment for dependency management.
C. Use Docker to containerize the project, ensuring that the dependencies and environment are consistent across different systems.
D. Use Conda to create isolated environments for different versions of dependencies, ensuring version compatibility.
E. Manually install all dependencies directly on the host machine to avoid using dependency management tools.

Answer: C,D

NEW QUESTION # 13
You are building a deep learning model using TensorFlow with cuDNN acceleration on an NVIDIA GPU. Your dataset contains continuous numerical features with vastly different ranges.
What is the best way to standardize the data efficiently to improve model convergence?

A. Use cuml.StandardScaler() from RAPIDS to normalize the dataset before feeding it into the model.
B. Manually compute the feature mean and variance on the CPU and apply the transformation before training.
C. Use cupy.linalg.norm() to normalize each feature vector individually to unit length.
D. Apply batch normalization layers in the neural network to handle feature scaling dynamically during training.

Answer: A

NEW QUESTION # 14
A retail company needs to design a real-time ETL workflow to process millions of sales records per second from multiple sources. The data needs to be cleaned, aggregated, and stored for real-time analytics.
Which NVIDIA-powered solution is best suited for this workload?

A. Use standard Apache Spark on CPU clusters and move transformed data to GPU storage
B. Use NVIDIA Spark RAPIDS to accelerate ETL processing within Apache Spark on GPUs
C. Perform ETL with cuGraph to model sales data as a graph before storage
D. Use NumPy to perform array-based operations on sales data before inserting into a database

Answer: B

NEW QUESTION # 15
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