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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - GPU architecture and fundamentals 1. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization - GPU resource management 1. Efficient GPU resource allocation and scheduling - Performance optimization 1. Mixed precision and bottleneck analysis 2. Memory profiling with DLProf 3. Single and multi-GPU performance optimization
Data Analysis	14%	- Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets
Data Preparation	17%	- Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance - 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 - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines
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. Selection of appropriate algorithms for GPU execution 2. Training models using cuML and GPU-accelerated XGBoost 3. Multi-GPU training strategies - Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Feature engineering for ML models 3. Batching and memory-efficient training methods
Data Manipulation and Software Literacy	19%	- Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency - 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. Data integration, joining, merging, and filtering 3. Groupby, apply, and aggregation operations
MLOps	19%	- 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 deployment and serving 1. Production deployment strategies 2. Model saving, loading, and prediction generation - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q141-Q146):

NEW QUESTION # 141
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. Force every operation to be recomputed from the raw dataset to ensure accurate results.
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. Use dask.persist() to store frequently accessed cuDF DataFrames in GPU memory, reducing recomputation and shuffle operations.

Answer: D

NEW QUESTION # 142
You are working on a machine learning dataset with millions of rows and want to optimize performance on a GPU using CuDF. One of the features in your dataset represents categorical data with a limited number of unique values.
Which of the following data types should you use to maximize performance while reducing memory usage?

A. int64
B. bool
C. object (string)
D. category

Answer: D

NEW QUESTION # 143
A data scientist is analyzing large-scale sensor readings from an industrial IoT system and wants to visualize high-frequency time-series data efficiently.
Which approach using NVIDIA technologies would be the most effective for interactive visualization of this dataset?

A. Use NVIDIA OptiX to ray-trace the time-series dataset for visualization.
B. Use RAPIDS cuDF with Datashader to render large time-series data on the GPU efficiently.
C. Process the data using RAPIDS cuDF, then convert it to CSV and visualize it with Excel charts.
D. Apply TensorRT to compress the time-series data and use PyTorch's native visualization functions.

Answer: B

NEW QUESTION # 144
You are consulting for a retail company that collects data from daily sales transactions, customer interactions, and inventory tracking across multiple locations. They are unsure whether their dataset qualifies as big data and which processing method would be most suitable.
Which of the following characteristics best indicate that the dataset requires big data processing and acceleration techniques?

A. The dataset contains more than 1 million records, making it impossible to process using pandas
B. The dataset exceeds the memory capacity of a single machine and requires distributed or GPU- accelerated processing
C. The dataset includes a mix of numerical and categorical variables, requiring additional preprocessing
D. The dataset is stored in multiple relational database tables, making querying inefficient

Answer: B

NEW QUESTION # 145
When deciding whether to use GPU acceleration or a traditional CPU approach for a machine learning task, which of the following factors should be considered to determine if the data qualifies as "big data" and whether GPU acceleration is beneficial? (Select two)

A. CPU-based machine learning methods are always more effective for small datasets, regardless of the algorithm used.
B. GPU acceleration is beneficial when the dataset can be divided into independent chunks that can be processed in parallel.
C. The complexity of the algorithm being used plays a crucial role in deciding whether to use GPU acceleration, with more complex algorithms benefiting from parallel computation.
D. The size of the dataset in terms of rows and columns is irrelevant when determining if it qualifies as big data.
E. The dataset must be over 100GB in size to qualify as big data and warrant GPU acceleration.

Answer: B,C

NEW QUESTION # 146
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