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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Manipulation and Software Literacy	19%	- Distributed computing with Dask 1. Dask-cuDF for parallel data processing 2. Scaling data operations across multiple GPUs - 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. Data integration, joining, merging, and filtering 2. cuDF vs pandas API mapping and usage 3. Groupby, apply, and aggregation operations
MLOps	19%	- Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - 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
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. Training models using cuML and GPU-accelerated XGBoost 2. Multi-GPU training strategies 3. Selection of appropriate algorithms for GPU execution - Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Batching and memory-efficient training methods 3. Feature engineering for ML models
Data Preparation	17%	- Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading
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 - Performance optimization 1. Mixed precision and bottleneck analysis 2. Single and multi-GPU performance optimization 3. Memory profiling with DLProf - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - GPU resource management 1. Efficient GPU resource allocation and scheduling
Data Analysis	14%	- Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is working on a social network analysis project where they need to find the most influential users in a large-scale graph dataset. The dataset consists of millions of users connected through directed edges.
Which of the following approaches would be the best choice for this task using NVIDIA GPU-accelerated tools?

A) Use cuDF's groupby().sum() function to count the number of connections per user.
B) Convert the graph into a pandas DataFrame and apply NetworkX's PageRank implementation.
C) Use cuGraph's pagerank() function to identify influential nodes based on link structure.
D) Use cuGraph's bfs() (Breadth-First Search) to find the most influential nodes.

2. You are processing a large dataset in a distributed computing environment using RAPIDS and Dask.
Your workflow involves frequent shuffling of data between partitions, leading to significant slowdowns.
Which of the following strategies is the best way to implement data caching to reduce shuffle overhead using NVIDIA technologies?

A) Enable GPU-accelerated caching with RAPIDS cuDF and persist intermediate results in GPU memory.
B) Disable caching altogether to force a recomputation of results, ensuring up-to-date data processing.
C) Use a CPU-based caching solution like Memcached to store intermediate data before reloading into cuDF.
D) Use traditional disk-based caching by writing intermediate results to CSV files and reloading when needed.

3. You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?

A) Hadoop MapReduce
B) RAPIDS + Dask for distributed GPU-accelerated ETL
C) Apache Spark with its default CPU-based execution
D) Pandas with multiprocessing

4. Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?

A) Data Preparation
B) Business Understanding
C) Model Building
D) Data Understanding

5. You are analyzing a time-series dataset that represents temperature readings from an industrial sensor. Your goal is to detect anomalies that may indicate sensor failures or environmental changes.
Which of the following methods would be the most appropriate statistical technique for detecting anomalies in this time-series dataset?

A) One-Hot Encoding
B) Moving Average Smoothing
C) Principal Component Analysis (PCA)
D) Z-score (Standard Score) Analysis

Solutions:

Question # 1
Answer: C

Question # 2
Answer: A

Question # 3
Answer: B

Question # 4
Answer: B

Question # 5
Answer: D

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