Key Features of the NCP-ADS certification course

The Practice tests give learners a brief understanding of the exam pattern and format to prepare.
These are perfect exam stimulators where the learners get to experience real-time exam scenarios.
Through consistent practice, you not only understand the strengths and weaknesses, but all gain confidence to attempt NCP ADS Exam.

Main Topics and Domains in the Accelerated Data Science Certification

The NCP-ADS certification validates the practical ability of a candidate in accelerating GPU data science workflows using the NVIDIA RAPIDS ecosystem. It emphasises hands-on problem-solving, using tools efficiently for large datasets.

The Main Topics and Domains Covered in the Exam

GPU-Accelerated DataFrames with cuDF

Work with cuDF
Cleaning, filtering, joining, and transforming data
Memory management and performance tuning

Machine Learning with cuML

Use cuML
Train and evaluate models
Model performance comparison

Graph Analytics with cuGraph

Graphical representations and GPU computation
Algorithms like PageRank, BFS, and community detection
Social network Applications, systems recommendation

Data Loading & ETL with DALI & RAPIDS

High-performance data ingestion with NVIDIA DALI
Building efficient ETL pipelines
Integrate Apache Arrow and other formats

Data Visualization

Visualise large datasets with Plotly, matplotlib, etc.
Exploratory data analysis with GPU-backed tools
Rendering data using RAPIDS-compatible libraries

End-to-End Workflow Design

Constructing full data science pipelines
Combining cuDF, cuML, DALI, and visualising a project
Real-time processing and batch workflows

Performance Tuning & Troubleshooting

Profiling GPU usage and memory management
Understanding bottlenecks in GPU workflows
Best practices for speed and stability

It also focuses on

Data analysis
Data manipulation and software literacy
Data preparation
GPU and cloud computing
Machine learning
MLOps

Tools and Frameworks in NVIDIA Certified Professional Accelerated Data Science

RAPIDS suite: cuDF, cuML, cuGraph
NVIDIA DALI: Data loading for deep learning
Python, Jupyter Notebooks
XGBoost (GPU-accelerated)
Visualisation: Plotly, matplotlib, etc

Skill Validated With NVIDIA-Certified Professional Accelerated Data Science

Processing GPU-Based Data
Machine Learning skills on GPUs
Ace Graph Analytics
High-performing Data Ingestion & ETL
Large Datasets visualisation
Workflow Execution end-to-end Data Science
Ability to tune & Troubleshoot

Who Are The Target Audience?

Data Scientists who wanted to accelerate end-to-end data science workflows using GPU-optimised libraries.
Data Engineers with NCP-ADS can build faster, scalable pipelines with GPU-accelerated tools.
Data Analysts shall unlock the rapid data exploration and visualisation at scale with ADS certification.
Machine Learning Engineers will train and deploy ML models with peak GPU performance.
AI DevOps Engineers can utilise it to streamline MLOps with GPU-powered model optimisation and deployment.
Applied Data Scientists can bring real-time insights to production using NVIDIA’s accelerated stack.
Software Engineers shall integrate high-performance data science into apps using NVIDIA tools.
Solution Architects can design AI-powered solutions using GPU-accelerated infrastructure.
Deep Learning Performance Engineers maximise training and inference speed on NVIDIA GPUS.
For researchers, it helps with high-volume, accelerated experiments with massive datasets.

What's Next After NCP-ADS Certification?

NVIDIA Certified Developer – AI Inference

To learn how to deploy and optimise deep learning models for real-time GPU inference.

NVIDIA Deep Learning Institute (DLI) Certificates

Get an expert in building and training neural networks with the GPUS.

TensorFlow Developer Certificate (by Google)

Empower your ability to build and deploy ML models using TensorFlow

NVIDIA DLI – Accelerated Computing with CUDA Python

Learn to speed up Python applications using CUDA for parallel processing.

AWS Certified Machine Learning – Speciality

Validates your skills to build and scale machine learning solutions on AWS.

Microsoft Certified: Azure AI Engineer Associate

Allows you to design and implement AI-driven applications using Azure services.

Google Cloud Professional Data Engineer

Learn to design data pipelines and scalable ML solutions.

Databricks Certified Machine Learning Professional

Master end-to-end ML workflows with Apache Spark and MLflow.

Certified Kubernetes Administrator (CKA)

Builds foundational skills to manage containerised AI/ML workloads.

Job Opportunities With NCP-ADS Certification

Data Engineer
HPC Data Analyst (High-Performance Computing)
Quantitative Analyst / Quant Developer
Research Scientist (AI/ML)
Big Data Analyst
Business Intelligence (BI) Developer (GPU-focused)
Data Scientist (GPU-Accelerated)
Machine Learning Engineer
AI Developer / Deep Learning Engineer