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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 - Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility
Topic 2: GPU and Cloud Computing	16%	- Performance optimization 1. Mixed precision and bottleneck analysis 2. Single and multi-GPU performance optimization 3. Memory profiling with DLProf - 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 - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud
Topic 3: Data Analysis	14%	- 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 - 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
Topic 4: Machine Learning	15%	- 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. Selection of appropriate algorithms for GPU execution 2. Multi-GPU training strategies 3. Training models using cuML and GPU-accelerated XGBoost - Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts
Topic 5: Data Manipulation and Software Literacy	19%	- 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. 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
Topic 6: Data Preparation	17%	- GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet - 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

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question #1

You are working on a medium-sized dataset (~500,000 rows, 20 columns) and need to perform fast exploratory data analysis (EDA) with filtering, aggregations, and transformations.
Which of the following Python libraries would be the most efficient choice for this task?

A. Vaex
B. Dask
C. Pandas
D. PySpark

Answer: C

Question #2

A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.
Which NVIDIA-supported tool is best suited for this purpose?

A. Rely on Matplotlib to generate static plots and update them every minute with a loop.
B. Precompute the time-series visualization with Dask and display it in a static HTML page.
C. Use Plotly Dash with RAPIDS cuDF to create an interactive GPU-powered dashboard.
D. Convert the stock price dataset into a NumPy array and visualize it using Seaborn's line plot.

Answer: C

Question #3

You are working on a large dataset for a machine learning model that will be trained using RAPIDS cuML. The dataset includes categorical, integer, and floating-point features.
Which of the following approaches is the best practice for determining the optimal data type choice for each feature using NVIDIA's RAPIDS cuDF library?

A. Use float16 for all floating-point data to reduce memory usage and increase GPU processing speed.
B. Use float32 instead of float64 for floating-point numbers when possible, and leverage int8, int16, or int32 for categorical and integer data based on their range.
C. Convert all numerical data to float64 for maximum precision in calculations.
D. Convert categorical variables into int8 to optimize GPU memory usage.

Answer: B

Question #4

A data scientist is using NVIDIA RAPIDS to perform statistical analysis as part of exploratory data analysis (EDA) on a dataset containing millions of product reviews. They need to compute basic descriptive statistics such as mean, median, and variance efficiently.
Which of the following methods is the most appropriate for performing these calculations on GPUs?

A. Use NumPy's statistical functions, such as numpy.mean() and numpy.var()
B. Use a traditional SQL database to compute statistics and then transfer results to the GPU
C. Convert the dataset into a PyTorch tensor and use PyTorch's statistical methods
D. Use cuDF's built-in statistical functions like .mean(), .median(), and .var()

Answer: D

Question #5

You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?

A. 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)
B. df = (df - df.mean()) / df.std()
C. df = (df - df.min()) / (df.max() - df.min())
D. df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)

Answer: A