• Exam Breakdown
  • Domain Breakdown
  • Access Breakdown

Exam Format

  • Exam Code: NCA-GENL
  • Certification Level: Associate
  • Exam Duration: 60 minutes
  • Passing Score: Not publicly disclosed by NVIDIA (score report provided after completion)
  • Unscored Content: The exam may include unscored questions for research purposes that do not affect your final score.

Exam Details

  • Question Types: Multiple choice, multiple response
  • Number of Questions: Approximately 50–60 questions (NVIDIA does not publish exact count)
  • Hands-On Questions: No hands-on lab simulations — focuses on generative AI and LLM concepts.

Exam Policies

  • Rescheduling/Cancellation: Must follow NVIDIA certification testing policies (typically at least 24 hours before scheduled exam time).
  • Waiting Period: Retake policies follow NVIDIA certification guidelines.
  • Retake Fee: Full exam fee required for each retake.

Certification Validity and Renewal

  • Validity: 2 years
  • Renewal Options: Pass the latest version of the certification exam or meet NVIDIA recertification requirements.

Exam Fee

  •  Base Fee: $200 USD (excluding taxes)
  •  Taxes: Country-specific VAT may apply
  •  Example: In India, 18% tax applies, making the total $236 USD ($200 + $36 tax)

Prerequisites

There are no mandatory prerequisites for taking the NVIDIA-Certified Associate: Generative AI LLMs exam. However, NVIDIA recommends:
  • Basic understanding of artificial intelligence and machine learning concepts
  • Familiarity with natural language processing (NLP) fundamentals
  • Understanding of deep learning and neural network concepts
  • Basic knowledge of GPU computing and AI workflows

Exam Topics

  • Generative AI Fundamentals — Generative AI concepts, model types, use cases, ethical considerations
  • Large Language Models (LLMs) — LLM architecture, transformers, tokenization, model training and inference
  • NLP and Deep Learning Concepts — Natural language processing workflows, embeddings, fine-tuning concepts
  • NVIDIA AI Ecosystem — NVIDIA GPUs, CUDA, AI frameworks, NVIDIA AI software stack
  • Model Deployment and Optimization — Inference pipelines, performance optimization, scalability considerations
  • Responsible AI and Security — Bias, safety considerations, data privacy, model governance

Intended Audience

  • The NVIDIA-Certified Associate: Generative AI LLMs certification is designed for professionals and learners working with AI technologies, including:
  •  AI Engineers (entry-level)
  • Data Scientists (beginner level)
  • Machine Learning Engineers (entry-level)
  • Software Developers working with AI
  • IT Professionals exploring generative AI technologies

Career Impact

Jobs You Can Get:
  • AI Engineer (entry-level), Machine Learning Engineer, AI Developer, Data Scientist (junior), AI Solutions Specialist
Average Salary:
  • U.S.: $85,000–$130,000 USD,
  • India: ₹8,00,000–₹20,00,000 INR,
  • United Kingdom: £45,000–£80,000 GBP,
  • UAE: 180,000–320,000 AED per year.

Why It’s Valuable:

  • Validates generative AI and LLM knowledge, demonstrates understanding of modern AI technologies, recognized by organizations adopting AI-driven solutions.

Exam Mode

The exam is typically delivered:
  • Online through NVIDIA certification testing platform
  • Through authorized NVIDIA certification partners (availability varies by region)

Exam Booking Link

Once you pass the exam:

  • Access your certification through the NVIDIA certification portal
  • Digital certificate available after successful completion
  • Download and share certification credentials from your NVIDIA account dashboard

Offers

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    Top Reasons to Choose
    NVIDIA-Certified Associate: Generative Al LLMs

    Growing Demand for Generative AI Skills

    Organizations are rapidly adopting generative AI technologies, creating strong demand for professionals who understand large language models and their real-world applications.

    Hands-On Understanding of LLM Architecture and Prompt Engineering

    This certification validates foundational knowledge of transformer models, prompt engineering techniques, and AI development workflows used for generative AI systems.

    Career Opportunities in AI and Machine Learning Development

    Certified professionals gain recognition for generative AI expertise, opening career paths in AI engineering, machine learning development, and AI-powered application design.

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    FAQ

    • Who should take the NVIDIA-Certified Associate: Generative AI LLMs exam?

      The NVIDIA-Certified Associate: Generative AI LLMs exam is designed for developers, data scientists, and IT professionals interested in generative AI technologies. It is ideal for individuals who want to understand how large language models work and how they are used to build AI-powered applications.

    • How difficult is the NVIDIA-Certified Associate: Generative AI LLMs exam?

      The exam is considered entry-level to intermediate and focuses on conceptual understanding of generative AI technologies. Candidates with basic knowledge of machine learning, natural language processing, and AI development typically find the exam manageable with proper preparation.

    • Why does NVIDIA offer the Generative AI LLMs certification?

      NVIDIA offers this certification to validate professionals who understand the fundamentals of generative AI and large language models. As generative AI becomes widely adopted across industries, organizations need professionals who can develop, deploy, and optimize AI-powered applications.

    • What tools and resources can be used to prepare for this exam?

      Candidates can prepare using NVIDIA Deep Learning Institute (DLI) training courses, official NVIDIA documentation, and generative AI learning resources. Studying transformer models, prompt engineering, and GPU-accelerated AI frameworks is recommended for successful exam preparation.

    • Is the NVIDIA-Certified Associate: Generative AI LLMs certification still valuable in 2026?

      Yes, the certification remains highly valuable in 2026 because generative AI technologies continue to grow rapidly across industries. Professionals who understand LLM architectures, AI frameworks, and generative AI workflows are increasingly important for developing modern AI applications.