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NVIDIA-Certified Associate Generative AI LLMs Certification Exam — Information & Free Practice

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About NVIDIA-Certified Associate Generative AI LLMs Certification Exam

NVIDIA-Certified Associate Generative AI LLMs Certification Exam · US

Key exam facts

Provider
Certiverse
Format
English multiple-choice examination; online remote delivery is subject to NVIDIA proctoring, identity verification and secure-browser policies.
Exam questions
Total questions: 50–60.
Time limit
1 hour
Passing score
Pass/fail; no score is reported. An exact passing threshold is not published in the reviewed materials.
Exam fee
USD 125

Topics covered

  • Core Machine Learning and AI Knowledge (30%)

    • Assisted model deployment, scalability, performance and reliability evaluation
    • Awareness of extracting insights from large datasets
    • Build LLM use cases
    • Curate and embed content datasets for RAGs
    • Familiarity with machine learning fundamentals
    • Familiarity with Python natural language package capabilities
    • Read research to identify emerging LLM trends and technologies
    • Select and use models to create text embeddings
    • Apply prompt engineering principles
    • Use Python packages for traditional machine learning analyses

  • Data Analysis (14%)

    • Awareness of extracting insights from large datasets
    • Compare models using statistical performance metrics
    • Conduct supervised data analysis
    • Create specialized data visualizations
    • Identify relationships, trends and factors affecting research results

  • Experimentation (22%)

    • Assist in supervised model training and optimization
    • Assist in dataset preparation for pretraining, fine-tuning and RLHF
    • Assist in LLM hardware or software test design and conduct
    • Assist in technology evaluation
    • Awareness of or participation in human data collection
    • Evaluate and refine existing models and benchmarking
    • Execute experiments to evaluate models and pipelines

  • Software Development (24%)

    • Assist in supervised model deployment and evaluation
    • Build LLM use cases
    • Familiarity with Python natural language package capabilities
    • Identify required system data, hardware and software components
    • Monitor data collection, experiments and software processes
    • Use Python packages for traditional machine learning analyses
    • Write supervised software components or scripts

  • Trustworthy AI (10%)

    • Describe ethical principles of trustworthy AI
    • Describe data privacy and consent balance
    • Describe technologies that improve AI trustworthiness
    • Describe minimizing AI bias

How to prepare

Work through the topics, read the explanation after each answer, and revisit the areas where you miss questions. Use the official outline and reference materials below to guide your study.

These independent practice questions are not actual exam items and do not guarantee a passing result.

Frequently asked questions

What is the exam format?

English multiple-choice examination; online remote delivery is subject to NVIDIA proctoring, identity verification and secure-browser policies.

How many questions are on the exam?

Total questions: 50–60.

How long is the exam?

1 hour

What is the passing score?

Pass/fail; no score is reported. An exact passing threshold is not published in the reviewed materials.

How much does the exam cost?

USD 125

Official sources and references

Confirm current exam arrangements with the exam provider.