Aegis-GPU

NCP-GENL · Professional · 120 min · 60–70 questions · $200

Generative AI LLMs

Blueprint + study plan Verified blueprint, study plan generator, and curated official resources. Practice content coming soon.

Start here

Practice questions for this exam are coming. Start with a study plan.

Build a study plan

Today's study plan

Enter your exam date and weekly hours for a day-by-day plan weighted by the official domains.

Build my plan

Readiness by domain

Tracks your accuracy and coverage in each official domain as you practise.

Mock exam

Unlocks when the question bank reaches 60 questions.

Exam details and more tools

Everything below is folded away to keep this page calm. Open a section, or switch on Advanced mode (in the More menu) to have them open by default.

More study tools

  • Ask Aegis: the tutor button at the bottom right answers from the course content with citations.
  • Xid error reference: every in-use NVIDIA Xid code with NVIDIA's recommended actions.
  • HGX hardware guide: a beginner's tour of an 8-GPU server.

Exam facts

Level
Professional
Duration
120 minutes
Questions
60–70
Price
$200
Delivery
Online, remotely proctored
Language
English
Validity
Two years from issuance
Scoring
Pass/fail (no numeric score is reported)
Prerequisites
2–3 years of practical experience in AI or ML roles working with large language models, with a solid grasp of transformer-based architectures, prompt engineering, distributed parallelism, and parameter-efficient fine-tuning. Familiarity with advanced sampling, hallucination mitigation, retrieval-augmented generation, model evaluation metrics, and performance profiling is expected. Proficiency in efficient coding (Python, plus C++ for optimization), experience with containerization and orchestration tools, and acquaintance with NVIDIA’s AI platforms is beneficial but not strictly required.

Exam facts verified against NVIDIA's NCP-GENL page on 2026-10-08. The official page gives one descriptive paragraph per domain (no bulleted objectives); it is reproduced as that domain's single objective. Always confirm on nvidia.com before booking.

Official blueprint

LLM Architecture — 6%
  1. 1.1 Understanding and applying foundational LLM structures and mechanisms.
Prompt Engineering — 13%
  1. 2.1 Adapting LLMs to new domains, tasks, or data distributions via prompt engineering, chain-of-thought (CoT), domain adaptation, zero/one/few-shot learning, and output control.
Data Preparation — 9%
  1. 3.1 Preparing data for pretraining, fine-tuning, or inference by cleaning, curating, analyzing, and organizing datasets, tokenization, and vocabulary management.
Model Optimization — 17%
  1. 4.1 Deploying LLMs in production environments. Includes building containerized inference pipelines, configuring model serving and orchestration (e.g., Kubernetes, NVIDIA Triton), implementing real-time monitoring, optimizing deployment for latency and throughput, and managing model updates.
Fine-Tuning — 13%
  1. 5.1 Creating conceptual data mapping documents, custom importers, exports, and scripts for interchange of data with OpenUSD.
Evaluation — 7%
  1. 6.1 Assessing LLMs via quantitative and qualitative metrics, framework design, benchmarking, error analysis, and scalable evaluation.
GPU Acceleration and Optimization — 14%
  1. 7.1 Scaling and optimizing LLM training/inference on GPU hardware. Involves multi-GPU/distributed setups, parallelism techniques, troubleshooting, memory and batch optimization, and performance profiling.
Model Deployment — 9%
  1. 8.1 Deploying LLMs in production via containerized pipelines, scalable orchestration, efficient batch/model serving, and real-time monitoring.
Production Monitoring and Reliability — 7%
  1. 9.1 Establishing monitoring dashboards and reliability metrics while tracking logs and anomalies for root cause analysis and benchmarking agents against previous versions. Implementing automated tuning, retraining, and versioning to ensure continuous uptime, transparency, and trust in production deployments.
Safety, Ethics, and Compliance — 5%
  1. 10.1 Responsible for AI practices throughout the LLM lifecycle. Includes auditing for bias and fairness, implementing guardrails, configuring monitoring for ethical compliance, and applying bias detection and mitigation strategies to ensure responsible deployment and use of LLMs.

Study roadmap

  1. Baseline. Read the official exam page and study guide, then skim every domain below to find gaps.
  2. Model Optimization (17%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  3. GPU Acceleration and Optimization (14%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  4. Prompt Engineering (13%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  5. Fine-Tuning (13%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  6. Data Preparation (9%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  7. Model Deployment (9%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  8. Evaluation (7%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  9. Production Monitoring and Reliability (7%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  10. LLM Architecture (6%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  11. Safety, Ethics, and Compliance (5%). Study the NVIDIA training and docs linked below for this domain; keep notes per objective.
  12. Exam rehearsal. Re-read your weakest domain notes and the study guide.

Official resources

From NVIDIA