NVIDIA-Certified Professional: Generative AI LLMs
NCP-GENL
The NCP-GENL exam tests whether you can train, fine-tune, optimize and deploy large language models on NVIDIA GPUs, with evaluation and production reliability in scope. The preporato.com prep for this NVIDIA generative AI LLM certification pairs timed practice exams, every question explained, with hands-on GPU labs.
420+
問題数
7
練習テスト
22
実践ラボ
無期限
アップデート
含まれる内容
模擬試験
本番形式の模擬試験7セット
実践
ラボ· 22
Deploy & Serve LLMs in Production (Jupyter)
ロック解除Fine-Tune an LLM with LoRA and QLoRA (Jupyter)
ロック解除CUDA Programming Fundamentals
ロック解除Quantize & Optimize LLMs with bitsandbytes
ロック解除Profile PyTorch Training with the Built-in Profiler
ロック解除Advanced RAG: Hybrid Search + Cross-Encoder Reranking
ロック解除Continued Pre-Training: Adapt a Pretrained LM to a New Domain
ロック解除NVIDIA DALI: GPU-Accelerated Data Pipelines
ロック解除Data Preparation for LLM Training
ロック解除RLHF & DPO Alignment
ロック解除Evaluation & Benchmarking LLMs
ロック解除Inference Serving Patterns: Dynamic Batching, Throughput, and the Triton Mental Model
ロック解除Nsight Systems Profiling: Finding the Bottleneck That Costs You 40% of Your GPU
ロック解除Batch Size & Precision Sweep: Finding Your Sweet Spot
ロック解除Retrieval-Augmented Generation (RAG) Pipeline with Local Models
ロック解除Reproducible Training: The Flags, The Cost, The Artifacts
ロック解除Fine-Tune Stable Diffusion with LoRA: Custom Text-to-Image
ロック解除Synthetic Data Generation for Model Training
ロック解除Train a Small Language Model from Scratch
ロック解除Build a Transformer from Scratch: Attention, Masking & LayerNorm
ロック解除vLLM Production Serving: PagedAttention, Continuous Batching, Prefix Caching
ロック解除Build NeMo Guardrails for an AI Agent: Jailbreak & Topical Rails
ロック解除学習を始めますか?
1回の購入で上記のすべてを利用できます。無期限アクセス、30日間返金保証。
なぜこの認定を取得するのか?
検証されるスキル
- Designing and training large language models
- Advanced prompt engineering (CoT, zero/one/few-shot learning)
- Fine-tuning techniques (Full, PEFT, LoRA, QLoRA)
- Distributed training and multi-GPU optimization
- Model quantization, pruning, and distillation
- +7個のスキル
キャリアの利点
対象職種
給与範囲
$140,000 - $250,000+
LLM engineering roles growing 40%+ annually as enterprises adopt generative AI
試験トピックとドメイン
認定試験は、10つの主要なコンピテンシー領域にわたってあなたの知識を評価します:
- Production deployment strategies
- Containerization and orchestration
- TensorRT-LLM optimization
- Quantization techniques (INT8, FP16, INT4)
- Pruning and distillation
- Accuracy vs latency trade-offs
- Multi-GPU setups and parallelism
- Distributed training strategies
- Performance profiling
- Tensor Core utilization
- Memory optimization
- DGX system configuration
- Chain-of-thought prompting
- Zero-shot and few-shot learning
- Domain adaptation techniques
- Prompt optimization strategies
- In-context learning
- Full fine-tuning approaches
- Parameter-efficient fine-tuning (PEFT)
- LoRA and QLoRA techniques
- Custom data mapping
- Model customization for specific use cases
- Instruction tuning
- Dataset curation and cleaning
- Tokenization strategies
- Vocabulary management
- Data augmentation
- Quality filtering
- Inference pipelines
- Real-time monitoring
- Batch vs streaming inference
- API design for LLM services
- Load balancing
- Benchmarking methodologies
- Error analysis techniques
- Evaluation metrics (perplexity, BLEU, ROUGE)
- Human evaluation frameworks
- A/B testing for LLMs
- Monitoring dashboards
- Uptime maintenance
- Logging and observability
- Incident response
- Performance degradation detection
- Transformer architectures
- Attention mechanisms
- Positional encodings
- Model scaling laws
- Architecture trade-offs
- Bias detection and mitigation
- Responsible AI practices
- Content filtering
- Hallucination prevention
- Privacy considerations
カバーされる技術とツール
できるようになること
この認定を取得した後、あなたは次のことができるようになります:
- Design and train production-ready large language models
- Implement advanced prompt engineering techniques for optimal performance
- Apply distributed training across multi-GPU environments
- Optimize models using quantization, pruning, and distillation
- Deploy LLMs at scale using NVIDIA NIM and Triton
- Fine-tune models using PEFT, LoRA, and QLoRA techniques
- Build and evaluate RAG pipelines for knowledge augmentation
- Monitor and maintain production LLM systems
- Implement responsible AI practices and safety guardrails
この資格が重要な理由
検証されるスキル
- Designing and training large language models
- Advanced prompt engineering (CoT, zero/one/few-shot learning)
- Fine-tuning techniques (Full, PEFT, LoRA, QLoRA)
- Distributed training and multi-GPU optimization
- Model quantization, pruning, and distillation
- TensorRT-LLM optimization for inference
- +6個のスキル
キャリアの利点
対象職種
給与範囲
$140,000 - $250,000+
LLM engineering roles growing 40%+ annually as enterprises adopt generative AI
積極的に採用している業界
よくある質問
The exam is intermediate-to-advanced level and requires 2-3 years of hands-on experience with production LLM systems. It covers 10 domains with heavy emphasis on Model Optimization (17%) and GPU Acceleration (14%). Candidates should have deep knowledge of distributed training, fine-tuning techniques, and NVIDIA's AI platform.
NVIDIA does not publicly disclose the exact passing score. The exam contains 60-70 questions and candidates have 120 minutes to complete it.
NCP-GENL (Professional) is an intermediate-level certification requiring 2-3 years of LLM experience, focusing on advanced topics like distributed training, model optimization, and production deployment. NCA-GENL (Associate) is entry-level, covering foundational LLM concepts. NCP-GENL has a longer exam (120 min vs 60 min) and costs more ($200 vs $125).
NVIDIA recommends: 'Building RAG Agents With LLMs' ($90, 8 hours), 'Adding New Knowledge to LLMs' ($500, 8 hours instructor-led), and 'Model Parallelism: Building and Deploying Large Neural Networks' ($500 instructor-led). Hands-on experience with distributed training on DGX systems is highly valuable.
No prerequisite certifications are required. However, having NCA-GENL provides a strong foundation. The exam assumes 2-3 years of practical experience with production LLM systems.
You should have experience with NVIDIA NIM, NeMo Framework, TensorRT-LLM, Triton Inference Server, distributed training frameworks (DeepSpeed, Megatron-LM), containerization (Docker/Kubernetes), and Python/C++ for optimization.
Yes, this is an official NVIDIA Professional-level certification validating advanced LLM skills. NVIDIA certifications are highly valued in the AI industry, with certified professionals commanding premium salaries in the $140K-$250K+ range.
Yes, the exam is delivered online and remotely proctored via the Certiverse platform, allowing you to take it from anywhere with a stable internet connection.
The exam costs $200 USD. You'll need to create a Certiverse account to register. NVIDIA occasionally offers promotional discounts.
合格に向けて始めましょう
模擬試験だけを購入することも、Proにアップグレードして実践ラボ22件に加えてPreporatoの他のすべてのコースを利用することもできます。
模擬試験のみ
無期限アクセス。一度の購入でずっと使えます
- フル模擬試験7セット
- 本番形式の問題420問以上
- 全問題に詳しい解説
- 試験モードと学習モード
- ×実践ラボは含まれません
Preporato Pro
月ごとの請求。いつでも解約できます
- NCP-GENL向けの実践ラボ22件 +他のAI/MLラボ53件
- すべての資格の模擬試験
- GPUサンドボックスとホスト環境
- フラッシュカード、学習ガイド、記事
- いつでも解約可能、契約の縛りなし