OpenExamsFREE PRACTICE — ANY EXAM

NVIDIA-Certified Associate: Generative AI LLMs — Requirements & Free Practice

US · License preparation

100 free questions · Answer explanations · No signup required

Loading selected examination banks…

Choose what to practice

Start with an exam below. Your examination requirements depend on your application pathway. View licensing requirements

Licenses, application pathways and examination requirements

Each license and applicant pathway has its own requirements and exemptions. Choosing practice questions does not determine your eligibility.

NVIDIA-Certified Associate: Generative AI LLMs

NVIDIA associate-level certification validating foundational concepts for generative AI and LLM applications with NVIDIA solutions; passing the NCA-GENL examination earns a digital badge and optional certificate, valid for two years from issuance.

Licensing authority: NVIDIA

Initial by examination
NVIDIA-Certified Associate Generative AI LLMs Certification Exam
Changes
Cancellation or rescheduling requires at least 24 hours; the rescheduled date must be within two months of payment. Non-cancellable and non-refundable within 24 hours.
Credential Validity
Two years from issuance; recertification by retaking the exam. This holder-specific duration is not an exam retirement date.
Delivery
Only the English online remotely proctored NCA-GENL examination is selected. NVIDIA’s freshly retrieved teaching-kit article explicitly describes this named examination as online and remotely proctored and links it to the current NCA-GENL page, whose account, format, duration and registration details remain consistent. The article’s May 31, 2026 limit governs the educational discount, not examination availability; no expired discount or educator-only free seat is claimed. Remote testing requires secure-browser installation, monitoring and recording, identity verification and environment assessment; no remote breaks are allowed.
Identification
At least one valid government-issued ID containing photograph and signature; registered name must match. The proctor also checks NVIDIA account information.
Prerequisite
A basic understanding of generative AI and large language models.
Registration
Create a Certiverse account; schedule and pay for the examination. The current owner page links directly to https://www.certiverse.com/#/checkout/nvidia/store-exam/NCA-GENL. The registration platform redirects to sign-in before showing protected appointment inventory; no account was created and no seat was booked.
Retakes
After failure, repurchase with a 14-day waiting period; at most five attempts in the 12 months beginning on the first purchase date.
Scheduling
Exams may be scheduled up to 60 days in advance. Actual seats throughout 2026-11-25 through 2027-01-31 have not been reserved or guaranteed; the booking window is not an exam-validity boundary.
Training
The owner describes training and readings as recommended preparation; no additional named certification, compulsory course, practical examination or writing examination is imposed in the current examination details or certification process.
Version Applicability
The fresh current owner page and current catalogue both identify NCA-GENL and offer registration. No official effective or retirement date was published in the reviewed current pages or found by targeted official-source retirement/replacement searches. Null date boundaries are retained; no historic archived page is used. The three-calendar-month cutoff is 2027-01-28, and the stricter task exclusion applies to any explicit retirement before 2027-03-01.

NVIDIA-Certified Associate Generative AI LLMs Certification Exam

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
Examination outline
  • 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
Examination sources