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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: MLOps	19%	- 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 - Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware - Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows
Topic 2: 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. cuDF vs pandas API mapping and usage 2. Data integration, joining, merging, and filtering 3. Groupby, apply, and aggregation operations
Topic 3: GPU and Cloud Computing	16%	- GPU resource management 1. Efficient GPU resource allocation and scheduling - Performance optimization 1. Memory profiling with DLProf 2. Mixed precision and bottleneck analysis 3. Single and multi-GPU performance optimization - 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 4: 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 - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling
Topic 5: Data Analysis	14%	- Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - 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 - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph
Topic 6: Machine Learning	15%	- Model training with GPU acceleration 1. Training models using cuML and GPU-accelerated XGBoost 2. Selection of appropriate algorithms for GPU execution 3. Multi-GPU training strategies - Feature engineering and hyperparameter tuning 1. Feature engineering for ML models 2. Batching and memory-efficient training methods 3. Hyperparameter tuning techniques - Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q111-Q116):

NEW QUESTION # 111
In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

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

Answer: B,D

NEW QUESTION # 112
You are working with a GPU-based cloud environment and need to optimize the memory usage for a dataset that contains a column item_id representing unique product IDs. The item_id values are large integers, and there are over 10 million distinct product IDs.
Which of the following is the most memory-efficient data type choice for this column?

A. df['item_id'] = df['item_id'].astype('float64')
B. df['item_id'] = df['item_id'].astype('string')
C. df['item_id'] = df['item_id'].astype('int64')
D. df['item_id'] = df['item_id'].astype('int32')

Answer: C

NEW QUESTION # 113
You are building a real-time recommendation system that processes high-frequency transactional data from millions of users.
The system must:
- Ingest and preprocess data efficiently
- Perform similarity computations for user-item recommendations
- Scale to handle rapid incoming transactions
Which of the following NVIDIA technologies is the best choice for this use case?

A. NVIDIA Triton Inference Server
B. NVIDIA NVTabular
C. RAPIDS cuGraph
D. CUDA Kernels with Custom C++ Code

Answer: B

NEW QUESTION # 114
Which of the following methods are commonly used to handle missing data in data analysis? (Select two)

A. Removing rows with missing data
B. Ignoring missing data entirely
C. Using the mode to impute missing values
D. Imputing with the mean or median of the feature
E. Using a fixed constant value for imputation

Answer: A,D

NEW QUESTION # 115
You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A. Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
B. Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.
C. Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.
D. Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.

Answer: C

NEW QUESTION # 116
......

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