NVIDIA Certified Associate Generative AI & LLMs Guide 2026
The NVIDIA Certified Associate: Generative AI and LLMs (NCA-GENL) is an entry-level certificate to boost your LLM career. It validates your knowledge in Generative AI and Large Language Models, and how to use them in the NVIDIA environment.
The Deloitte report states that 37% of companies use GenAI and 34% are transforming their processes. These numbers are a clear indication that Generative AI LLM careers are on the rise, and it is important to keep up.
But you must start with the foundational knowledge if you aren’t well-versed with how AI tools operate.
In this blog, we will guide you on how to become an NVIDIA Certified Associate Generative AI and LLMs (NCA-GENL) professional. This certification is a valuable credential if you want to showcase your AI and LLM skills. It is beneficial for anyone looking to advance their skills or career growth.
Our NVIDIA-Certified Associate Generative AI LLMs study guide provides insights on:
- What is the role of GenAI LLMs in career growth?
- How to pass the NVIDIA GenAI LLM Exam on the first try?
- What are the topics expected in the NVIDIA GenAI LLM Certification Exam?
- How difficult is it to pass the NVIDIA GenAI LLM exam?
Let’s dive in!
What are Generative AI LLM Careers?
Generative AI LLM career paths focus on designing, using, and improving models for business requirements. The role requires skills integrating AI on a large scale to boost productivity and compete in the digitally transforming world. As companies are adopting GenAI massively, choosing an LLM career can be a quick growth booster for you in 2026.
Why Generative AI LLM Careers Are Growing in 2026
Another Deloitte report suggests that 24% of companies are already leveraging AI and 74% of companies will at least moderately use the technology by 2028. The numbers suggest that generative AI jobs in 2026 are on the rise, and if you are not advancing yourself, you are losing the race. Companies are not using AI to boost their workforce productivity but to completely revamp their operations model.
In 2026, job opportunities like AI engineer, data scientist, AI content strategist, and brand voice curator are coming up, while old roles are getting obsolete.
Here is why Large Language Model jobs are significantly increasing:
1. Digital Transformation
Be it healthcare, IT, or finance, every business is adopting AI to streamline processes, improve decision-making, and outrun the competition. AI is beneficial in implementing quick solutions and accelerating growth.
2. Automation Requirement
Everything is automated today. Whether it is mundane tasks like data entry or sophisticated operations like predicting growth. AI takes over these requirements so humans can manage strategic operations.
3. The Boom of Generative AI
The continuous improvement of AI tools has rapidly increased their demand in the corporate world. If you check out any job description today, one of the key skills companies look for is AI understanding.
4. Talent Gap
There is a shortage of skilled professionals and a huge demand in the AI market. The result? Intense competition which pushes companies to create new job roles and increase salaries.
So, beginners and professionals looking for a boost in their careers are encouraged to develop Generative AI roles and skills.
NVIDIA Generative AI Certification Overview
This NVIDIA exam is an entry-level certification designed to assess your foundational skills in Generative AI and LLMs in the NVIDIA solutions areas.
To pass the NCA Generative AI LLMs (NCA-GENL) exam, you must focus on the following key topics:
- Basic concepts of machine learning and neural networks
- Engineering effective prompts
- Alignment strategies
- Data analysis and visualisation techniques
- Experimentation methodologies
- Preprocessing data and feature engineering
- Designing experiments
- Software development principles
- Utilisation of Python libraries for LLMs
- Integration and deployment of LLMs
Understanding these topics acts as a foundation to build your knowledge and prepare you for the associate exam. It helps you clear the exam confidently in the first attempt.
NCA Generative AI LLMs Exam Syllabus
Below is a comprehensive overview of the certification exam syllabus:
1. Advanced Topics
A large portion of the NVIDIA-certified associate generative AI LLMS exam questions cover advanced key topics, including CuDF data frames, XGBoost GPU-accelerated machine learning, cuGraph graph analysis, and RAPIDS data science pipelines.
Some more important topics to cover when preparing for the associate certification exam are:
- NVIDIA Hardware (GPUs)
- NVIDIA Platforms (NEO, Jetson)
- Seminal Research Papers (e.g., “Attention Is All You Need”, Word2Vec)
- Machine Learning Fundamentals
- Data Science Principles
- Probability Theory
- Natural Language Processing (NLP)
- Transformers and LSTM Networks
- Activation Functions
- Gradient Descent and Related Concepts
2. Core AI Principles and NVIDIA-Specific Knowledge
The remaining portion of the exam focused on fundamental AI concepts, including neural network basics, NVIDIA’s infrastructure and AI development services, memory mapping techniques for machine learning, and mastering the skill of effective prompting in AI applications.
3. In-Depth Focus
Particularly challenging are the questions on AI model quantisation and the application of the transformer model. So, it is better to focus on this area more to tackle the challenges.
NVIDIA Certified Associate Generative AI LLMs Exam Technical Content Coverage
The NVIDIA Certified Associate in Generative AI and LLMs Exam assesses your proficiency in these areas:
- You can expect 10% of the NVIDIA GenAI LLMS Exam questions from general deep learning concepts, such as support vector machines (SVM), exploratory data analysis (EDA), and activation and loss functions.
- Another 10% focus on transformer architecture to cover topics like encoding, decoding, and attention mechanisms.
- Approximately 40% of the syllabus focuses on how to work with the NLP and LLM models. The questions are based on text normalisation techniques like stemming and lemmatisation and embedding mechanics like Word2Vec and WordNet. Other topics are python libraries, NLP evaluation frameworks, and interoperability standards.
- Only 40% of the exam content aligned with actively studied topics, with a focus on customisation, TensorRT, Triton Inference Server, and optimisation techniques for GPUs, CPUs, and memory in the NVIDIA stack. Some questions may directly relate to products like DGX, AI Enterprise, and NeMo. There can be questions on real-life applications of cuDF, cuML, and the NGC catalogue.
NVIDIA doesn’t disclose the exact subtopics they ask for in the exam. But they specifically mentioned the certification is for associate-level developers who have the foundational understanding of GenAI and LLMs.
LLM Career Paths in Generative AI
Will AI replace jobs? This question has been in every professional’s mind for the longest time.
The truth is, AI impacts several jobs today. In fact, McKinsey states that AI will boost the global economy by $13 trillion by 2030. However, it does not mean humans will be no more needed. The rise in GenAI LLM career paths means that the job roles will be more strategic and less labour-intensive.
Which New Generative AI LLM Careers Will Thrive?
Human guidance is still required, regardless of how proficient AI becomes. Here are some of the career paths you can choose in 2026:
- AI and Machine Learning Engineers
- Data Scientists
- Ethical Hackers
- Cloud Architects
- Governance Experts
- UX/UI Designers
- Synthetic Data Engineer
- GenAI Model Trainer
- AI Ethics Analyst
And many more.
The NCA-GENL course acts as the first step to building your foundation as an AI professional.
Generative AI Jobs 2026: Roles, Skills, and Salary Trends
The salary package for any AI expert depends on the roles and responsibilities in an organisation. Below is a quick estimated overview for your understanding:
|
Job title |
Skill set required |
Salary package (estimated) |
| AI engineer |
|
₹11,00,000 p.a. |
| ML engineer |
|
₹10,80,000 p.a. |
| Data Engineer |
|
₹10,00,000 p.a. |
| Robotics Engineer |
|
₹5,00,000 p.a. |
| Software Engineer |
|
₹9,00,000 p.a. |
| Data Scientist |
|
₹15,00,000 p.a. |
How to study for the NVIDIA Certified Associate Exam?
Follow this guide and prepare for the NCA-GENL exam so you can clear it on the first attempt:
Step 1: Understand exam objectives and format
Begin by understanding the objectives and exam format of the NVIDIA-Certified Associate certification. It is foundational knowledge where you can focus on key areas and design your study plan accordingly.
Visit the official NVIDIA website to access the exam objectives and guidelines.
Here is the exam format:
NVIDIA Certified Associate Exam Format |
|
|---|---|
| Duration | One hour |
| Price | $135 |
| Certification Level | Associate |
| Subject | Generative AI and large language models |
| Number of questions | 50 |
| Prerequisites | Basic understanding of generative AI and large language models |
| Language | English |
NVIDIA Gen AI and LLMs certification has a time limit of 60 minutes for 50 questions designed to test your understanding. The questions cover the technical aspects of Generative AI and LLMs, their implementation, and NVIDIA solutions.
Familiarise yourself with the above format and objectives so you can easily proceed with the preparations.
Design a study plan accordingly and cover the important topics for concept clarity.
Step 2: Make Your Foundation Strong
To start your NVIDIA Generative AI LLM journey, it’s essential to establish a strong foundation in NVIDIA Products. Begin by grasping fundamental concepts and techniques that machine learning algorithms are built on.
Resources like online courses, textbooks, and interactive projects can aid in this foundational understanding.
NVIDIA offers various free introductory and deep-dive courses, some of which are instructor-led and rather pricey.
Here are some fundamental and advanced courses released by NVIDIA:
- Foundational Deep Learning: Beginners and individuals seeking to refresh their knowledge can explore courses such as “Basics of Deep Learning” on Udemy and “Deep Learning Fundamentals” by Lightning AI, providing a robust understanding of deep learning principles and their applications across diverse industries.
- Accelerated Data Science: Delve into courses like “AI Application Boost with NVIDIA RAPIDS Acceleration” to learn how to optimise data science tasks with GPU acceleration, fostering accelerated data processing and analysis.
- Natural Language Processing Proficiency: You can enhance your NLP skills with the comprehensive “Hugging Face NLP Course”, offering practical insights into building applications using Transformer models. Additionally, gain a practical understanding of BERT through resources like the “BERT NLP Presentation” on Scribd.
- Customising LLMs: You can check out the “Large Language Model Customisation Course” on Udemy if you are an advanced learner. It focuses on tailoring LLMs to suit your specific requirements.
- Building a Retrieval-Augmented GenAI Model: Understand the complexities of creating AI agents on DeepLearning.AI. You can enrol in the Building Agentic RAG with LLAMA” course. It offers valuable insights into the techniques.
Step 2: Understanding NVIDIA Products
Mastering the NVIDIA generative AI certification requires you to gain knowledge of the products and solutions and their importance. These products include:
- NeMo: The open-source toolkit for conversational AI. It supports the creation of high-end models.
- cuOPT: The GPU software suite is used to optimise complex systems efficiently.
- TensorRT: This platform is used for high-performance deep learning inference.
- NVIDIA Cloud: Cloud-based solutions offer flexible AI and data science computing resources.
- NVIDIA DGX: The series of high-performance computing systems are known to scale AI training and inference workloads.
The exam focuses on a comprehensive understanding and application of AI and LLM models. It showcases the importance of learning how to implement GenAI in real-life situations.
Step 3: Programming skills
The NVIDIA Generative AI LLMs exam requires basic coding skills, especially in programming languages. These languages are commonly used in machine learning and artificial intelligence, such as Python.
User-friendly tools and platforms allow you to create AI models with minimal coding. But if you want to create complex models, a deep understanding of programming concepts is important.
Some of the valuable sources are libraries and frameworks like TensorFlow, PyTorch, or Keras. Also, proficiency in handling data, understanding algorithms, and debugging code is also important.
Step 4: Participate in Hands-on Projects
Only theory isn’t sufficient to boost your LLM career paths. You must know how to apply the knowledge and tackle the complex organisational challenges.
For instance, gaining contextual knowledge of GenAI is not enough. As a professional, you have to know how to use it to generate art, images, and music. That’s where Whizlabs hands-on labs help by giving you situations like real life. You can use your knowledge to handle the given complexities.
Step 5: Take the NVIDIA Generative AI practice tests to know your readiness
Prepare yourself for the actual exam through Whizlabs practice tests. These exams follow the NVIDIA exam pattern to assess your skills and how you perform under pressure. Our tests cover all important concepts so you can acquire the certification confidently.
Try to score at least 80% on our practice tests so you know you are ready to appear for the actual exam.
How Difficult Is the NVIDIA Generative AI Certification for LLM Careers?
The difficulty level is moderate to challenging, depending on your concept clarity. If you have a deep understanding of the topics, you can clear the exam clearly. So, ensure that you study through course material like videos and notes on deep learning, NLP applications, and model customisation.
Study and hands-on learning of AI technologies and NVIDIA tools can help you pass the exam on the first attempt.
The exam may look difficult. But with continuous learning, preparation, and understanding, you can overcome the challenges.
Generative AI Practice Tests for LLM Careers
Clearing the exam is your first step to building a strong LLM engineer career path. In this journey, practice tests help you prepare for the certification by giving you a complete picture of how the actual exam will be. You can benefit from:
- Assessing your knowledge of AI and LLM concepts
- Identifying your strengths and areas for improvement
- Figuring out the approach to attempt the real exam
- Learning how to perform well under pressure
FAQs
1. What is the NVIDIA Certified Associate in Generative AI and LLMs Exam?
The NVIDIA Certified Associate Generative AI and LLMs (NCA-GENL) is an entry-level certification that validates your knowledge of GenAI and LLMs. it also proves that you are proficient in NVIDIA’s tools and frameworks.
2. How much time does it take to learn the Generative AI LLMs?
You can prepare yourself in 3 to 4 weeks. But the actual time depends on your knowledge and skills. We recommend you spend 2-3 hours a week to get a complete understanding of the exam concepts.
3. Who should take this NVIDIA Certified Associate exam?
This exam is ideal for AI professionals, data scientists, and machine learning engineers who want to build foundational knowledge and apply NVIDIA AI solutions in real life.
4. What topics are covered in the exam?
The exam covers general deep learning concepts, transformer architecture, NLP and LLMs. It also focuses on NVIDIA-specific tools and optimisation techniques.
5. What is the duration of the NVIDIA Certified Associate in Generative AI and LLMs exam?
The duration of the associate exam is 60 minutes and includes 50 multiple-choice questions.
6. What is the exam cost of the NVIDIA Certified Associate in Generative AI and LLMs exam?
The exam cost of the NVIDIA Certified Associate certification is USD 135.
Wrapping Up
The NVIDIA Certified Associate Certification prepares you for the jobs that are lining up in 2026. But the first step is learning and preparation to pass the examination. Although the exam can feel like a pressure, especially if you are a working individual, well-organised study material helps.
Whizlabs makes the study material and mock tests easily accessible for you so you are ready for the actual exam. Our platform offers updated practice questions and video courses for thorough understanding. We also offer hands-on labs and cloud sandboxes so you can apply the theoretical knowledge and understand its relevance. What more? Get your Gen AI career moving confidently with Whizlabs.
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