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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Manipulation and Software Literacy	19%	- GPU-accelerated data manipulation using cuDF 1. Groupby, apply, and aggregation operations 2. Data integration, joining, merging, and filtering 3. cuDF vs pandas API mapping and usage - 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
Machine Learning	15%	- Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch - Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Hyperparameter tuning techniques 3. Feature engineering for ML models - Model training with GPU acceleration 1. Multi-GPU training strategies 2. Selection of appropriate algorithms for GPU execution 3. Training models using cuML and GPU-accelerated XGBoost
GPU and Cloud Computing	16%	- GPU resource management 1. Efficient GPU resource allocation and scheduling - 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 - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud
MLOps	19%	- Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - 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
Data Preparation	17%	- GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - 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 - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables
Data Analysis	14%	- Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - 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:

Question #1

You need to deploy a machine learning model on a GPU-equipped system. The GPU has 16GB of VRAM, and the model requires approximately 12GB of memory during inference. However, additional system processes and other applications consume 5GB of VRAM.
What would happen if you attempt to run inference without making any optimizations, and how should you resolve the issue?

A. Switching from a GPU to CPU inference will resolve memory issues without performance loss
B. The model will fail to run due to out-of-memory (OOM) errors, and using a smaller batch size can help reduce memory usage
C. The model will run without issues because 16GB of VRAM is sufficient for a 12GB model
D. The model will run successfully but with reduced performance due to memory fragmentation

Reveal Solution Discussion 0

Correct Answer: B 🗳️

Question #2

You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?

A. Convert both DataFrames to Pandas before performing the join for compatibility
B. Perform the join operation entirely in a CPU-based Spark environment for better stability
C. Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining
D. Store the transaction data as a CSV file and perform joins using SQL queries before loading it into cuDF

Reveal Solution Discussion 0

Correct Answer: C 🗳️

Question #3

You are training a deep learning model on a large dataset and are deciding whether to use a single GPU or multiple GPUs.
Which of the following are true considerations when comparing single-GPU and multi-GPU training setups? (Select two)

A. Multi-GPU training requires modifications to the model architecture to make it compatible with parallel processing.
B. Single-GPU training is generally more cost-effective and should be preferred unless scaling is absolutely necessary.
C. Multi-GPU training can significantly reduce training time when the dataset is large and the model is computationally intensive.
D. Single-GPU training is limited by the VRAM (video memory) on the GPU, so larger models or datasets may require multi-GPU setups.
E. Multi-GPU setups perform better only when the batch size is reduced.

Reveal Solution Discussion 0

Correct Answer: C,D 🗳️

Question #4

You are tasked with processing a large dataset using multiple GPUs to accelerate computation. You decide to use Dask to implement data parallelism with NVIDIA's RAPIDS framework to maximize GPU utilization.
Which of the following steps is essential for efficiently distributing the workload across multiple GPUs in Dask?

A. Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.
B. Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.
C. Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.
D. Use dask.dataframe.repartition() to distribute data evenly across multiple GPUs.

Reveal Solution Discussion 0

Correct Answer: C 🗳️

Question #5

Your data science team is performing exploratory data analysis (EDA) on a large GPU-accelerated environment using cuDF and Dask-cuDF. During analysis, queries on categorical columns are performing poorly.
Which approach will most effectively improve query performance for categorical data in GPU-accelerated DataFrames?