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NCA-GENL Practice Tests

NVIDIA Generative AI LLMs Associate

Prepare for the NVIDIA-Certified Associate Generative AI LLMs exam with practice for AI fundamentals, LLM application development, experimentation, data analysis, visualization, and trustworthy AI.

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Duration

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Questions

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Cost

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Where to register
NVIDIA

Issued by NVIDIA. Delivered via Certiverse. NVIDIA provides an exam blueprint, preparation resources, and registration through Certiverse.

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01·Overview

Certification overview

The format, prerequisites, and what to expect on exam day.

Exam details

  • Exam Code

    NCA-GENL

  • Duration

    Check NVIDIA registration details

  • Questions

    Check NVIDIA registration details

  • Format

    Certification exam

  • Passing Score

    Check NVIDIA registration details

  • Cost

    Check Certiverse registration

  • Validity

    Check NVIDIA certification policy

Prerequisites
  • Foundational understanding of machine learning, deep learning, transformers, and large language models
  • Ability to build or integrate LLM-powered applications
  • Familiarity with prompt engineering, RAG, fine-tuning concepts, data preparation, and trustworthy AI
02·Domains

Exam domains

Topics on the official blueprint, with their relative weight.

01
Core Machine Learning and AI Knowledge
30%
  • Machine learning fundamentals
  • Deep learning concepts
  • Transformer architectures
  • Foundation models and LLMs
  • Embeddings and tokenization
02
Software Development
24%
  • Python for AI applications
  • Calling LLM APIs and SDKs
  • Building LLM-powered applications
  • Integration patterns
  • RAG application development
03
Experimentation
22%
  • Prompt engineering
  • Fine-tuning and PEFT
  • LoRA and QLoRA
  • Hyperparameter selection
  • Evaluating model variants
04
Data Analysis and Visualization
14%
  • Data preparation for LLM training and tuning
  • Quality assessment and curation
  • Statistical analysis of model outputs
  • Training and evaluation metrics
05
Trustworthy AI
10%
  • Responsible AI principles
  • Bias, fairness, and transparency
  • Hallucinations and guardrails
  • Governance and compliance for LLMs
03·Key topics

What you actually study

Service families and concept clusters that show up across questions.

LLM Fundamentals

  • Transformers
  • attention
  • foundation models
  • tokenization
  • embeddings

Application Development

  • Python
  • APIs
  • RAG
  • vector databases
  • LLM orchestration

Experimentation

  • Prompting
  • fine-tuning
  • LoRA
  • QLoRA
  • NeMo concepts

Trustworthy AI

  • NeMo Guardrails
  • bias
  • safety
  • governance
  • hallucination mitigation
04·Study tips

How to actually pass it

Practical strategies for the weeks before, and the morning of.

Preparation strategy
  • Start with transformer and LLM fundamentals before moving into NVIDIA-specific tooling.
  • Practice identifying when to use prompting, RAG, fine-tuning, or guardrails.
  • Review basic Python application patterns for calling LLM APIs and handling retrieved context.
  • Study trustworthy AI concepts as part of every LLM application lifecycle.
Exam day
  • Identify the product family first, then match the answer to the service or platform named in the scenario.
  • Watch for requirements around cost, latency, governance, and operational ownership.
  • Use elimination on answers that ignore security, monitoring, or responsible AI requirements.
  • Flag long architecture questions and return after completing faster recall questions.
  • Choose managed services when the question emphasizes speed, governance, and reduced operations.

Build associate-level LLM confidence.

Practice core AI, LLM development, experimentation, data, and trust scenarios. Start free, no card required.

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