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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation	17%	- 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 - 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
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. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - GPU resource management 1. Efficient GPU resource allocation and scheduling - Performance optimization 1. Mixed precision and bottleneck analysis 2. Single and multi-GPU performance optimization 3. Memory profiling with DLProf
Data Analysis	14%	- Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - 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 - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals
Data Manipulation and Software Literacy	19%	- 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 - Distributed computing with Dask 1. Dask-cuDF for parallel data processing 2. Scaling data operations across multiple GPUs - Software literacy and development tools 1. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
MLOps	19%	- Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Model deployment and serving 1. Production deployment strategies 2. Model saving, loading, and prediction generation - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools
Machine Learning	15%	- Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts - 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 - Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Hyperparameter tuning techniques 3. Feature engineering for ML models

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q293-Q298):

NEW QUESTION # 293
You are working on a data science project using NVIDIA RAPIDS on a multi-GPU system.
To ensure reproducibility and avoid software versioning conflicts, which of the following is the best approach for managing dependencies?

A. Use a Conda environment with RAPIDS-compatible versions of libraries installed using conda install
-c rapidsai -c nvidia.
B. Use a manually compiled CUDA installation alongside system-installed Python libraries to manage GPU dependencies.
C. Install all required packages globally on the system using pip install without a virtual environment.
D. Avoid dependency management frameworks and rely on manual tracking of package versions using a text file.

Answer: A

NEW QUESTION # 294
A data science team wants to leverage GPU acceleration for detecting anomalies in a massive IoT sensor dataset that is continuously streaming.
Which of the following NVIDIA-supported methods would be the best for handling real-time anomaly detection in a high-throughput environment?

A. Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection.
B. Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.
C. Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.
D. Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies.

Answer: C

NEW QUESTION # 295
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 Triton Inference Server to distribute random forest model training across multiple GPUs.
B. NVIDIA TensorRT to accelerate random forest model training by optimizing tree-based algorithms.
C. NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.
D. NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.

Answer: D

NEW QUESTION # 296
You are working on a large-scale machine learning pipeline that involves processing massive datasets using multiple GPUs on an NVIDIA DGX system. You choose to use Dask to enable efficient parallel processing across multiple GPUs.
Which of the following steps is essential to correctly configure Dask for multi-GPU acceleration?

A. Assign computation tasks explicitly to CPUs using dask.config.set({'scheduler': 'threads'}) before using GPUs.
B. Use dask_cuda.LocalCUDACluster() to create a cluster of GPU workers and pass it to the Dask client.
C. Use dask.distributed.Client() without specifying a scheduler to automatically detect available GPUs.
D. Avoid using dask_cudf and instead rely on standard pandas DataFrames to ensure GPU-accelerated execution.

Answer: B

NEW QUESTION # 297
In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A. Lower cost of cloud infrastructure
B. Better for memory-intensive workloads
C. Lower energy consumption compared to CPUs
D. Faster parallel processing for large datasets
E. Efficient handling of matrix operations in machine learning models

Answer: D,E

NEW QUESTION # 298
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