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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: MLOps	19%	- Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools - 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 - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation
Topic 2: Data Preparation	17%	- GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet - Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading
Topic 3: Machine Learning	15%	- 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. Batching and memory-efficient training methods 2. Feature engineering for ML models 3. Hyperparameter tuning techniques - Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts
Topic 4: GPU and Cloud Computing	16%	- Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - 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
Topic 5: Data Analysis	14%	- Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib
Topic 6: Data Manipulation and Software Literacy	19%	- Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing - 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. Groupby, apply, and aggregation operations 3. cuDF vs pandas API mapping and usage

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which tools or technologies from NVIDIA are essential for implementing an efficient MLOps pipeline in production environments? (Select two)

A) NVIDIA TensorRT for efficient model inference
B) NVIDIA CUDA for model training in cloud environments
C) NVIDIA DLA (Deep Learning Accelerator) for model deployment
D) NVIDIA Triton Inference Server for managing deployment and serving models
E) NVIDIA NGC for storing and sharing machine learning datasets

2. You are working with a dataset containing billions of rows and need to perform data transformations, aggregations, and joins efficiently on a single-node GPU-enabled workstation.
Which NVIDIA technology is best suited to optimize performance for these operations?

A) NVIDIA Nsight Compute to profile and optimize the performance of GPU-based aggregations.
B) NVIDIA TensorRT to optimize DataFrame transformations and aggregations using deep learning.
C) NVIDIA Triton Inference Server to accelerate data processing workflows on a single GPU.
D) NVIDIA RAPIDS cuDF to leverage GPU acceleration for large-scale DataFrame operations.

3. You are training a machine learning model using RAPIDS cuML and need to ensure that all numeric features are standardized for better model performance.
Which of the following is the best approach for scaling data using RAPIDS?

A) scaler = cuml.preprocessing.StandardScaler()
B) df_scaled = df.apply(lambda x: x / np.linalg.norm(x))
C) df_scaled = (df - df.min()) / (df.max() - df.min())
D) df_scaled = df / df.max()
E) df_scaled = scaler.fit_transform(df)

4. A data scientist is preprocessing a dataset containing multiple categorical features using NVIDIA RAPIDS to accelerate feature engineering.
The dataset contains:
A low-cardinality categorical feature (Product Type) with 10 unique values.
A high-cardinality categorical feature (User ID) with 100,000 unique values.
A numerical feature (Price) that requires transformation.
Which of the following feature engineering approaches will be the most efficient for GPU acceleration?

A) Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency.
B) Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32.
C) Apply one-hot encoding to both Product Type and User ID, and scale Price using float64 precision.
D) Frequency encoding for User ID is an efficient alternative to one-hot encoding, as it replaces each category with its frequency in the dataset, reducing dimensionality while preserving useful information.
E) Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.
F) Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price.

5. You are designing an ETL pipeline to process terabytes of financial transaction data in real time.
The pipeline consists of:
Extracting data from multiple sources (CSV, Parquet, and SQL databases), Transforming the data using operations such as filtering, joins, and aggregations, Loading the processed data into a data lake for analytics.
Given that you are using NVIDIA RAPIDS cuDF for GPU-accelerated ETL, which of the following approaches optimizes performance while ensuring scalability?

A) Convert cuDF DataFrames to Pandas DataFrames before performing transformations for compatibility
B) Use cuDF to read and process the data in batches, leveraging Dask-cuDF for distributed computation when necessary
C) Use CPU-based ETL frameworks such as Apache Spark without GPU acceleration
D) Load all data into a single, large cuDF DataFrame before performing transformations

Solutions:

Question # 1
Answer: A,D

Question # 2
Answer: D

Question # 3
Answer: A

Question # 4
Answer: B

Question # 5
Answer: B