- Definitions and distinctions between AI, ML, and generative AI.
- Overview of large language models and their impact on technology.
- NVIDIA's role in advancing generative AI.
- Key applications of LLMs in industry.
NVIDIA Certified Associate exam preparation
NVIDIA Certified Associate: Generative AI LLMs Certification Training Course
The NVIDIA Certified Associate Generative AI LLMs Certification Training Course with Accumentum offers an in-depth exploration of artificial intelligence (AI), machine learning (ML), with a special focus on generative AI and large language models (LLMs), tailored specifically to NVIDIA's infrastructure and operations.
Course focus: This course is designed for professionals who manage or work with AI technologies like business analysts, IT managers, and sales professionals. It delves into fundamental AI concepts, practical applications of LLMs, and ethical considerations, utilizing NVIDIA's tools such as NeMo for model customization and deployment.
Course Overview
The NVIDIA Certified Associate Generative AI LLMs Certification Training Course with Accumentum offers an in-depth exploration of.
The NVIDIA Certified Associate Generative AI LLMs Certification Training Course with Accumentum offers an in-depth exploration of artificial intelligence (AI), machine learning (ML), with a special focus on generative AI and large language models (LLMs), tailored specifically to NVIDIA's infrastructure and operations. This course is designed for professionals who manage or work with AI technologies like business analysts, IT managers, and sales professionals. It delves into fundamental AI concepts, practical applications of LLMs, and ethical considerations, utilizing NVIDIA's tools such as NeMo for model customization and deployment. Participants will dive into the NVIDIA AI ecosystem, learning how to pinpoint opportunities for LLM applications, grasp design considerations for generative models, and understand the significance of responsible AI practices. The course also features modules on security, compliance, and governance tailored to generative AI, preparing learners for the NVIDIA Certified Associate Generative AI LLMs certification exam. By the end of the training, attendees will have the knowledge to drive business innovation through generative AI and comprehend its strategic impact on organizational strategies.
Course Objectives
Course objectives for the NVIDIA Certified Associate: Generative AI LLMs Certification Training Course.
01
Understand the principles of generative AI, focusing on
Understand the principles of generative AI, focusing on large language models (LLMs), and learn how to leverage NVIDIA's infrastructure and tools like NeMo for model training and deployment.
02
Gain hands-on experience in applying LLMs to real-world
Gain hands-on experience in applying LLMs to real-world scenarios, understanding how to customize, fine-tune, and scale these models for various use cases.
03
Develop a deep understanding of ethical considerations in
Develop a deep understanding of ethical considerations in generative AI, including bias mitigation, privacy protection, and responsible AI deployment with NVIDIA's ethical AI frameworks.
04
Equip yourself with the knowledge and skills necessary
Equip yourself with the knowledge and skills necessary to pass the NVIDIA Certified Associate Generative AI LLMs exam, focusing on performance optimization, security, compliance, and governance in the context of LLMs.
Who Should Attend
- Those who design, develop, or maintain AI models, particularly interested in expanding their expertise into generative AI and LLMs using NVIDIA technologies.
- Professionals involved in data analysis who aim to implement or enhance their workflows with generative AI capabilities for advanced data interpretation and generation.
- Individuals responsible for architecting AI solutions within organizations, looking to incorporate NVIDIA's generative AI tools for scalable and efficient AI deployment.
- Executives, managers, or consultants who need to understand the strategic implications of generative AI to make informed decisions about technology adoption and innovation.
Prerequisites
- Basic AI and Machine Learning Knowledge: A fundamental understanding of AI concepts, including machine learning principles and neural networks.
- Programming Skills: Proficiency in programming languages commonly used in AI, such as Python, with some experience in machine learning frameworks like PyTorch or TensorFlow.
- Familiarity with NVIDIA Technologies: Basic knowledge of NVIDIA's hardware (like GPUs) and software platforms relevant to AI, including CUDA or NVIDIA NGC.
- Experience with Data Handling: Practical experience in working with large datasets, data preprocessing, and understanding data pipelines for AI model training.
Pathway Map
10 modules covering the NCA-GENL exam domains.
Course modules: Introduction to Generative AI and LLMs, Fundamentals of LLMs, NVIDIA's Infrastructure for Generative AI, NVIDIA NeMo for LLM Development, Data Preparation for LLMs, Model Training and Fine-Tuning, LLM Deployment and Inference, Ethical Consi.
1
Introduction to Generative AI and LLMs
2
Fundamentals of LLMs
3
NVIDIA's Infrastructure for Generative AI
4
NVIDIA NeMo for LLM Development
5
Data Preparation for LLMs
6
Model Training and Fine-Tuning
7
LLM Deployment and Inference
8
Ethical Considerations in Generative AI
9
Security, Compliance, and Governance for LLMs
10
Future Trends and NVIDIA's Vision
Course Content
- Architecture of transformer models.
- Language model training techniques and challenges.
- Understanding model parameters and their significance.
- Pre-training vs. fine-tuning in LLMs.
- NVIDIA GPUs and their specifications for AI workloads.
- DGX systems for high-performance AI computing.
- NVIDIA AI platforms tailored for LLMs.
- Performance metrics for LLM training and inference.
- Introduction to NeMo and its ecosystem.
- Customizing models with NeMo frameworks.
- Model optimization and scaling with NeMo.
- Deployment strategies using NeMo.
- Data collection strategies for LLM training.
- Data cleaning and augmentation techniques.
- Handling multilingual and domain-specific data.
- Efficient data pipeline design for LLMs.
- Techniques for efficient LLM training.
- Fine-tuning pre-trained models for specific tasks.
- Transfer learning in the context of LLMs.
- Managing training resources and costs.
- Strategies for deploying LLMs in production.
- Optimizing inference speed with NVIDIA TensorRT.
- Scaling inference for real-world applications.
- Monitoring and managing deployed models.
- Bias detection and mitigation in LLMs.
- Privacy concerns and data security.
- Ethical AI frameworks from NVIDIA.
- Responsible use of generative AI in society.
- Security best practices for AI models.
- Compliance with AI regulations.
- Governance models for AI projects.
- Incident response and model updates in production.
- Emerging trends in generative AI technology.
- NVIDIA's roadmap for advancing LLMs.
- Integration of AI with emerging tech like edge computing.
- Preparing for future challenges in AI ethics and performance.
Course Features
Interactive NCA-GENL exam preparation with Accumentum.
Interactive Learning
Engage with expert instructors and peers through training sessions, discussions, and practical exercises.
Comprehensive Study Materials
Access extensive resources, including e-books, video lectures, and practice exams.
Real-World Applications
Work on real-life case studies and scenarios to apply NVIDIA LLM concepts.
Certification Preparation
Receive guidance and tips to successfully pass the NVIDIA Certified Associate Generative AI LLMs Certification exam.
Certification Exam
Prepare for the official NVIDIA NCA-GENL exam.
NCA-GENL Exam Readiness
Accumentum NCA-GENL training prepares learners for the NVIDIA NCA-GENL certification exam.
Upon completing the NVIDIA Certified Associate Generative AI LLMs Certification Training Course with Accumentum, you will be fully prepared to sit for the NVIDIA Certified Associate Generative AI LLMs exam. This certification confirms your foundational understanding of generative AI, large language models, and NVIDIA's specialized infrastructure for these technologies. It showcases your ability to recognize opportunities for LLM applications, implement ethical AI practices, and adeptly use NVIDIA's tools like NeMo for model development and deployment. Earning this certification will significantly advance your career, positioning you for roles that require strategic foresight in generative AI and leadership in leveraging NVIDIA's ecosystem for innovative AI solutions.
Exam Blueprint
What the NCA-GENL exam covers.
NVIDIA splits NCA-GENL into 5 topic areas with 33 listed topics. We've copied each one below as NVIDIA publishes it, with its exam weight.
Core Machine Learning and AI Knowledge (30%)
Knowledge of algorithms, conventions, and techniques that allow computers to learn from and make predictions or decisions based on data.
- Assist in deployment and evaluation of model scalability, performance, and reliability under the supervision of senior team members.
- Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
- Build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers.
- Curate and embed content datasets for RAGs.
- Familiarity with the fundamentals of machine learning (for example feature engineering, model comparison, cross validation).
- Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
- Read research papers (articles, conference papers, etc.) to identify emerging LLM trends and technologies.
- Select and use models to create text embeddings.
- Use prompt engineering principles to create prompts to achieve desired results.
- Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
Data Analysis (14%)
Inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.
- Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
- Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
- Conduct data analysis under the supervision of a senior team member.
- Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
- Identify relationships and trends or any factors that could affect the results of research.
Experimentation (22%)
Perform, evaluate, and interpret experiments, including AI model evaluation and the use of human subjects in labeling or reinforcement learning from human feedback (RLHF).
- Assist in model training and training optimization under the supervision of a senior team member.
- Assist in preparing (for example scraping, tokenization) large datasets for pretraining, fine-tuning, and RLHF.
- Assist in the design and conduct of hardware or software tests for LLM applications.
- Assist in the evaluation of current or emerging technologies to consider factors such as cost, portability, compatibility, or usability.
- Awareness of, and/or participation in, data collection from human subjects (for example RLHF).
- Evaluate and refine existing models / benchmarking.
- Executes experimentation to evaluate models and pipelines.
Software Development (24%)
Create, maintain, and test software.
- Assist in the deployment and evaluations of model scalability, performance, and reliability under the supervision of a senior team member.
- Build LLM use cases such as RAGs, chatbots, and summarizers.
- Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
- Identify system data, hardware, or software components required to meet user needs.
- Monitor functioning of data collection, experiments, and other software processes.
- Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
- Write software components or scripts under the supervision of a senior team member.
Trustworthy AI (10%)
Creation and assessment of ethical, energy-conscious, and reliable artificial intelligence systems capable of interpreting and integrating various forms of data, ensuring that they're designed and applied in a manner that's transparent, fair, and verifiable.
- Describe the ethical principles of trustworthy AI.
- Describe the balance between data privacy and the importance of data consent.
- Describe how to use NVIDIA and other technologies to improve AI trustworthiness.
- Describe how to minimize bias in AI systems.
Who It Is For
Is NCA-GENL the right fit for you?
NVIDIA describes the candidate as an associate developer who contributes to the development, programming and quality assurance of generative AI LLM systems. They work with a team to develop datasets, select and train models, and implement testing and debugging. In our classes that means junior ML engineers, software developers moving into AI, data analysts and solution engineers who build RAG and chatbot prototypes.
NVIDIA's stated prerequisite is a basic understanding of generative AI and large language models. NVIDIA's learning path lists Fundamentals of Deep Learning and Rapid Application Development With Large Language Models (LLMs).
Exam Format
NCA-GENL at a glance.
Exam details
- Level: Associate
- Questions: 50-60 multiple choice
- Length: 1 hour
- Price: $125
- Language: English
- Scoring: pass/fail, with no score reported
- Validity: 2 years from issuance
NVIDIA certification exams are pass/fail, so you won't receive a numeric score. For most exams you see your result on screen when you finish, and an email follows within 24 hours along with your Credly badge invitation.
Breaks aren't allowed for remotely proctored exams, so plan to sit the full hour. Remote exams run in a secure browser from an NVIDIA Authorized Testing Partner, with identity checks and a 360 degree room scan before you start.
Study Plan
How we prepare you for NCA-GENL.
We start with Core Machine Learning and AI Knowledge. It's 30% of the exam, so we cover its 10 topics with short explainers and hands-on notebooks.
Next we cover Data Analysis. It's 14% of the exam, so we cover its 5 topics with short explainers and hands-on notebooks.
Then we move to Experimentation. It's 22% of the exam, so we cover its 7 topics with short explainers and hands-on notebooks.
After that comes Software Development. It's 24% of the exam, so we cover its 7 topics with short explainers and hands-on notebooks.
We wrap up with Trustworthy AI. It's 10% of the exam, so we cover its 4 topics with short explainers and hands-on notebooks.
Core Machine Learning and AI Knowledge carries the most weight at 30%, so it gets the most review time. In the last week we work through NVIDIA's practice questions in learning mode, revisit your weakest areas and reread the topic list line by line.
Exam Policies
Retakes and renewal for NCA-GENL.
If you don't pass, you can buy the exam again after a 14-day waiting period. You can't take the same exam more than five times in 12 months, counted from the day you purchased the first attempt.
You can schedule an exam up to 60 days in advance. The name on your government-issued ID must match the name you registered with, and no watches, phones or other devices are allowed in the testing area.
NVIDIA certifications are valid for two years, after which you retake the exam to recertify.
Career Outlook
Where NCA-GENL can take you.
NCA-GENL is aimed at developers building LLM applications. BLS doesn't track AI roles separately, so we use the closest occupation as a reference.
For a sense of pay, BLS puts the May 2025 median annual wage for software developers, quality assurance analysts and testers at $134,040. Treat it as a reference point and not a promise. Location, experience and the employer drive what any one person earns. Source: BLS Occupational Outlook Handbook.
Funding
Ways to pay for NCA-GENL training.
Our funding page lists the ways to pay for NCA-GENL training, and you can always talk with us or email info@accumentum.net before you enroll.
Frequently Asked Questions
NCA-GENL with Accumentum: audience, prerequisites, course content, exam, and enrollment.
The NVIDIA Certified Associate Generative AI LLMs Certification Training Course with Accumentum offers an in-depth exploration of artificial intelligence (AI), machine learning (ML), with a special focus on generative AI and large language models (LLMs), tailored specifically to NVIDIA's infrastructure and operations. This course is designed for professionals who manage or work with AI technologies like business analysts, IT managers, and sales professionals. It delves into fundamental AI concepts, practical applications of LLMs, and ethical considerations, utilizing NVIDIA's tools such as NeMo for model customization and deployment.
Those who design, develop, or maintain AI models, particularly interested in expanding their expertise into generative AI and LLMs using NVIDIA technologies. Professionals involved in data analysis who aim to implement or enhance their workflows with generative AI capabilities for advanced data interpretation and generation. Individuals responsible for architecting AI solutions within organizations, looking to incorporate NVIDIA's generative AI tools for scalable and efficient AI deployment. Executives, managers, or consultants who need to understand the strategic implications of generative AI to make informed decisions about technology adoption and innovation.
Basic AI and Machine Learning Knowledge: A fundamental understanding of AI concepts, including machine learning principles and neural networks. Programming Skills: Proficiency in programming languages commonly used in AI, such as Python, with some experience in machine learning frameworks like PyTorch or TensorFlow. Familiarity with NVIDIA Technologies: Basic knowledge of NVIDIA's hardware (like GPUs) and software platforms relevant to AI, including CUDA or NVIDIA NGC. Experience with Data Handling: Practical experience in working with large datasets, data preprocessing, and understanding data pipelines for AI model training.
10 course-content areas: Introduction to Generative AI and LLMs, Fundamentals of LLMs, NVIDIA's Infrastructure for Generative AI, NVIDIA NeMo for LLM Development, Data Preparation for LLMs, Model Training and Fine-Tuning, LLM Deployment and Inference, Ethical Considerations in Generative AI, Security, Compliance, and Governance for LLMs, Future Trends and NVIDIA's Vision.
Interactive Learning: Engage with expert instructors and peers through training sessions, discussions, and practical exercises. Comprehensive Study Materials: Access extensive resources, including e-books, video lectures, and practice exams. Real-World Applications: Work on real-life case studies and scenarios to apply NVIDIA LLM concepts. Certification Preparation: Receive guidance and tips to successfully pass the NVIDIA Certified Associate Generative AI LLMs Certification exam.
Upon completing the NVIDIA Certified Associate Generative AI LLMs Certification Training Course with Accumentum, you will be fully prepared to sit for the NVIDIA Certified Associate Generative AI LLMs exam. This certification confirms your foundational understanding of generative AI, large language models, and NVIDIA's specialized infrastructure for these technologies. It showcases your ability to recognize opportunities for LLM applications, implement ethical AI practices, and adeptly use NVIDIA's tools like NeMo for model development and deployment. Earning this certification will significantly advance your career, positioning you for roles that require strategic foresight in generative AI and leadership in leveraging NVIDIA's ecosystem for innovative AI solutions.
Enroll in the NVIDIA Certified Associate Generative AI LLMs Certification Training Course with Accumentum to advance your expertise in generative AI and large language models, securing a prestigious certification. This course is your guide to becoming a certified expert in utilizing NVIDIA's platforms for generative AI. For comprehensive details and to reserve your place, visit Accumentum's registration page linked below. You can also request guidance through Accumentum’s contact page.
NVIDIA lists 50-60 questions in 1 hour.
NVIDIA lists the price as $125.
NVIDIA exams are pass/fail, and NVIDIA doesn't report a score.
If you don't pass, you can buy the exam again after a 14-day waiting period. You can't take the same exam more than five times in 12 months, counted from the day you purchased the first attempt.
NVIDIA certifications are valid for two years, after which you retake the exam to recertify.
Enrollment
Enroll in NVIDIA Certified Associate: Generative AI LLMs Certification Training Course with Accumentum.
Enroll in the NVIDIA Certified Associate Generative AI LLMs Certification Training Course with Accumentum to advance your expertise in generative AI and large language models, securing a prestigious certification. This course is your guide to becoming a certified expert in utilizing NVIDIA's platforms for generative AI. For comprehensive details and to reserve your place, visit Accumentum's registration page linked below.
Use Accumentum’s registration page to enroll, or request guidance through the contact page.
Start NCA-GENL training with Accumentum.
Enroll in the NVIDIA Certified Associate Generative AI LLMs Certification Training Course with Accumentum to advance your expertise in generative AI and large language models, securing a prestigious certification.