NCP-GENL Practice Tests

NVIDIA Generative AI LLMs Professional

Prepare for NVIDIA-Certified Professional Generative AI LLMs with advanced practice for GPU acceleration, model optimization, prompting, fine-tuning, architecture, data, evaluation, deployment, monitoring, and safety.

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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 lists the professional exam registration and blueprint from the certification page.

01·Overview

Certification overview

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

Exam details

Exam Code
NCP-GENL
Duration
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Questions
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Format
Certification exam
Passing Score
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Cost
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Validity
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Prerequisites

Professional-level experience with production LLM systems
Familiarity with GPU acceleration, model optimization, fine-tuning, deployment, monitoring, and safety
Understanding of NVIDIA NIM, Triton, TensorRT-LLM, NeMo concepts, and production inference tradeoffs

02·Domains

Exam domains

Topics on the official blueprint, with their relative weight.

GPU Acceleration and Optimization

14%

CUDA fundamentals for LLM workloads
Mixed precision training
Tensor and pipeline parallelism
Distributed training
TensorRT-LLM optimization

Model Optimization

17%

Quantization
Pruning and distillation
KV-cache strategies
Speculative decoding
Inference engine selection

Prompt Engineering

13%

Zero-shot, few-shot, and chain-of-thought prompting
ReAct and tool use
Prompt templates and chaining
Structured output

Fine-Tuning

13%

Supervised fine-tuning
LoRA, QLoRA, and adapters
RLHF and DPO
Instruction tuning
NeMo Customizer workflows

Architecture, Data, Evaluation, Deployment, Monitoring, and Safety

43%

Transformer architecture and data curation
Benchmarks and LLM-as-a-judge
Triton, NIM, and Kubernetes serving
Latency, throughput, drift, and capacity monitoring
Guardrails, privacy, and compliance

03·Key topics

What you actually study

Service families and concept clusters that show up across questions.

Optimization

CUDA
TensorRT-LLM
quantization
mixed precision
distributed training

Customization

SFT
LoRA
QLoRA
RLHF
DPO
NeMo workflows

Deployment

NIM microservices
Triton Inference Server
Kubernetes
multi-GPU serving
vLLM

Operations

Monitoring
SLOs
A/B testing
drift detection
safety evaluation

04·Study tips

How to actually pass it

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

Preparation strategy

Study the lifecycle from model architecture and data preparation through deployment and monitoring.
Drill model optimization choices: quantization, distillation, KV-cache tuning, speculative decoding, and inference engine selection.
Practice recognizing when a scenario needs fine-tuning, prompt engineering, RAG, or deployment optimization.
Review safety, privacy, guardrails, and monitoring as production requirements, not afterthoughts.

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.

Prepare for professional LLM systems questions.

Practice optimization, customization, deployment, monitoring, and safety. Start free, no card required.

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