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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization
Topic 2: MLOps	19%	- 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. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation
Topic 3: Data Preparation	17%	- Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - Data loading and preprocessing 1. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet
Topic 4: Data Manipulation and Software Literacy	19%	- Software literacy and development tools 1. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) - 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. Data integration, joining, merging, and filtering 2. Groupby, apply, and aggregation operations 3. cuDF vs pandas API mapping and usage
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. Feature engineering for ML models 2. Batching and memory-efficient training methods 3. Hyperparameter tuning techniques - 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
Topic 6: 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. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science - GPU resource management 1. Efficient GPU resource allocation and scheduling - Performance optimization 1. Single and multi-GPU performance optimization 2. Mixed precision and bottleneck analysis 3. Memory profiling with DLProf

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is setting up a RAPIDS AI environment for a machine learning project that requires CUDA-enabled libraries and specific package versions to avoid conflicts.
Which of the following approaches best ensures a stable and reproducible environment while leveraging NVIDIA technologies?

A) Use a system-wide Python installation and install packages globally to ensure consistency across different users.
B) Install CUDA and RAPIDS AI libraries using OS-level package managers such as apt or yum instead of Conda or Docker.
C) Manually install each required package with pip to ensure the latest versions are used without interference from Conda.
D) Use Conda with the rapidsai channel to create an isolated environment that includes cuDF, cuML, and other GPU-accelerated libraries.

2. Which of the following best describes a key advantage of using cloud-based GPU instances for machine learning model training?

A) Cloud-based GPU instances offer lower latency and better network performance compared to on- premise deployments, regardless of geographical location.
B) Cloud GPUs are always more cost-effective than on-premise GPUs, as they do not incur long-term usage costs.
C) Cloud GPUs provide dynamically scalable resources, allowing users to increase or decrease compute power based on demand without upfront hardware investment.
D) Cloud GPU instances cannot support containerized workloads, limiting their applicability for MLOps and CI/CD pipelines.

3. In a typical MLOps pipeline, which of the following practices are essential to ensuring robust deployment and monitoring of machine learning models in production? (Select two)

A) Use of manual intervention for every model update to ensure accuracy.
B) Post-deployment data drift detection to assess model performance degradation.
C) Continuous integration and continuous deployment (CI/CD) pipelines for model updates.
D) Automated hyperparameter tuning during inference to optimize model performance.

4. You are analyzing a large financial dataset containing stock market tick-by-tick data stored in a cuDF DataFrame. Since the dataset contains billions of data points, you need to aggregate it at the minute level before visualizing price trends efficiently.
Which of the following is the best approach for aggregating and visualizing this time-series data using NVIDIA technologies?

A) Use cuML's TSNE function to reduce dimensionality before visualizing with Bokeh
B) Use cuDF's .groupby() function to aggregate at the minute level, then visualize using hvPlot
C) Convert cuDF to Pandas, aggregate using .resample() in Pandas, and visualize using Matplotlib
D) Load the data into a relational database (e.g., PostgreSQL), run an SQL query for aggregation, and visualize using Seaborn

5. You are working with a large dataset on an NVIDIA GPU, where optimizing memory usage is a priority. Your dataset contains a column, transaction_id, which stores unique integer values ranging between 0 and 100,000.
Which of the following data types is the most memory-efficient choice for this column in cuDF?

A) df['transaction_id'] = df['transaction_id'].astype('float32')
B) df['transaction_id'] = df['transaction_id'].astype('int32')
C) df['transaction_id'] = df['transaction_id'].astype('int8')
D) df['transaction_id'] = df['transaction_id'].astype('int64')

Solutions:

Question # 1
Answer: D

Question # 2
Answer: C

Question # 3
Answer: B,C

Question # 4
Answer: B

Question # 5
Answer: B