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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.

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.

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.

NVIDIA NCA-GENL Generative AI LLMs Practice Tests | ExamCoachAI | ExamCoachAI