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NCP-GENL Study Plan: 8-Week Preparation Guide [2026]

Preporato TeamFebruary 8, 202615 min readNCP-GENL

TL;DR: Pass the NVIDIA NCP-GENL certification in 8 weeks with 15-20 hours/week. Focus heavily on distributed training (Week 3-4) and hands-on fine-tuning (Week 5-6). Complete at least 4 full practice exams before your test date.

The NVIDIA Certified Professional: Generative AI and LLMs (NCP-GENL) requires both theoretical knowledge and hands-on experience. This 8-week plan is designed for professionals with 2+ years of ML experience who can dedicate 15-20 hours weekly.

Exam Quick Facts

Duration
120 minutes
Cost
$400 USD
Questions
60-70 questions
Passing Score
70%
Valid For
2 years
Format: Remote Proctored (Examity)

Prerequisites Check

Before starting this plan, ensure you have:

  • Python proficiency: Comfortable with PyTorch/TensorFlow
  • GPU access: At least a T4 GPU (Colab, Paperspace, or Lambda Labs)
  • ML foundations: Understand neural networks, backpropagation, optimization
  • Transformer basics: Familiar with attention mechanism concepts

If you're missing prerequisites, add 2-4 weeks of foundational study first.

Study Plan Overview

Weekly Time Commitment

WeekHours/WeekFocusHands-On %
Week 115Foundations30%
Week 215Prompting & Architecture40%
Week 318Distributed Training50%
Week 420Optimization & TensorRT-LLM60%
Week 520Fine-Tuning (LoRA/QLoRA)70%
Week 618Deployment & Triton60%
Week 715Evaluation & Responsible AI40%
Week 812Practice Exams & Review20%

Total: ~133 hours over 8 weeks

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Week 1: Transformer Foundations (Days 1-7)

Goal: Build deep understanding of transformer architecture and attention mechanisms.

Core Topics
  • •Self-attention mechanism and computation
  • •Multi-head attention and why it matters
  • •Positional encoding (absolute, relative, rotary)
  • •Encoder-only vs decoder-only vs encoder-decoder architectures
  • •Model scaling laws (Chinchilla scaling)
  • •Tokenization strategies (BPE, WordPiece, SentencePiece)
Skills Tested
Calculate attention complexity O(n²)Explain why transformers replaced RNNsCompare BERT, GPT, and T5 architectures
Example Question Topics
  • Draw the data flow through a transformer block
  • Calculate memory requirements for attention with different sequence lengths

Daily Schedule

DayTopicActivityHours
Day 1Transformer architectureRead "Attention Is All You Need" paper, annotate key sections2.5
Day 2Self-attention deep diveImplement scaled dot-product attention from scratch2.5
Day 3Multi-head attentionCode multi-head attention, understand projections2.0
Day 4Positional encodingCompare sinusoidal, learned, and RoPE encodings2.0
Day 5Architecture variantsStudy BERT, GPT-2, T5 code implementations2.5
Day 6TokenizationImplement BPE from scratch, use tiktoken library2.0
Day 7Week 1 ReviewTake diagnostic practice test, identify gaps1.5

Hands-On Labs

  1. Lab 1.1: Implement attention mechanism in PyTorch (no libraries)
  2. Lab 1.2: Visualize attention patterns using BertViz
  3. Lab 1.3: Compare tokenization outputs across different tokenizers

Resources

Week 1 Checkpoint

Week 1 Completion Checklist

0/6 completed

Week 2: Advanced Prompting & Model Variants (Days 8-14)

Goal: Master prompt engineering techniques and understand model architecture choices.

Daily Schedule

DayTopicActivityHours
Day 8Zero/few-shot learningExperiment with OpenAI API, document results2.5
Day 9Chain-of-thoughtImplement CoT prompting, measure accuracy difference2.5
Day 10Self-consistencyBuild voting system for multiple CoT paths2.0
Day 11Context window optimizationTest different context lengths, analyze trade-offs2.0
Day 12Model selection criteriaCompare Llama 2, Mistral, Mixtral for different tasks2.5
Day 13In-context learningDeep dive into how ICL works mechanically2.0
Day 14Week 2 ReviewComplete Domain 1 practice questions1.5

Hands-On Labs

  1. Lab 2.1: Build a CoT reasoning evaluator
  2. Lab 2.2: Implement self-consistency decoding
  3. Lab 2.3: Create prompt templates for classification, extraction, summarization

Prompt Engineering Comparison

Prompting Strategies by Task Type

Task TypeBest StrategyExample FormatAccuracy
Simple classificationZero-shotClassify this review as positive or negative: {text}85-90%
Complex classificationFew-shot (3-5 examples)Examples: ... Now classify: {text}92-95%
Math problemsChain-of-thoughtThink step by step: {problem}70-80%
Critical decisionsSelf-consistency (5+ paths)Multiple CoT + majority vote85-90%

Week 2 Checkpoint

Week 2 Completion Checklist

0/6 completed

Week 3: Distributed Training Fundamentals (Days 15-21)

Goal: Understand parallelism strategies and memory optimization techniques.

Critical Week

This is the most technically demanding section and the #1 reason candidates fail. Don't rush—ensure you truly understand when to use each parallelism strategy.

Daily Schedule

DayTopicActivityHours
Day 15Data parallelismImplement DDP training, measure scaling efficiency3.0
Day 16Tensor parallelismStudy Megatron-LM paper, understand column/row splitting2.5
Day 17Pipeline parallelismImplement micro-batching, understand bubble ratio2.5
Day 18DeepSpeed ZeROConfigure ZeRO Stage 1, 2, 3 on same model3.0
Day 19Memory optimizationGradient checkpointing, activation offloading2.5
Day 20Mixed precisionImplement AMP training, understand loss scaling2.5
Day 21Week 3 ReviewComplete parallelism practice problems2.0

Parallelism Decision Framework

When to Use Each Parallelism Strategy

ScenarioRecommended StrategyWhy
Model fits in single GPUData parallelism onlySimplest, fastest communication
Model slightly too largeZeRO Stage 2 + Data parallelismMinimal overhead, good scaling
70B+ model trainingZeRO-3 + Tensor parallelismMaximum memory efficiency
Very deep model (100+ layers)Pipeline + Tensor + DataBalances compute and memory
Limited inter-node bandwidthPipeline parallelismLess frequent communication

Hands-On Labs

  1. Lab 3.1: Train GPT-2 with PyTorch DDP across 2+ GPUs
  2. Lab 3.2: Configure DeepSpeed ZeRO stages for Llama-7B
  3. Lab 3.3: Implement gradient checkpointing, measure memory savings

Key Formulas to Memorize

Week 3 Checkpoint

Week 3 Completion Checklist

0/6 completed

Week 4: TensorRT-LLM & Inference Optimization (Days 22-28)

Goal: Master inference optimization techniques for production deployment.

Daily Schedule

DayTopicActivityHours
Day 22TensorRT-LLM introInstall, convert Llama model, benchmark3.0
Day 23Quantization (INT8)Apply PTQ and QAT, compare accuracy3.0
Day 24Quantization (INT4)Implement AWQ and GPTQ, benchmark3.0
Day 25KV cache optimizationUnderstand paged attention, implement caching2.5
Day 26Batching strategiesConfigure in-flight batching, measure throughput2.5
Day 27Speculative decodingImplement draft model verification2.5
Day 28Week 4 ReviewComplete optimization practice exam3.5

Quantization Methods Comparison

Quantization Methods for Production

MethodBitsCalibrationQualitySpeed
FP1616None100% (baseline)2x vs FP32
INT8 PTQ8Data sample~99%2-3x vs FP16
INT8 QAT8During training~99.5%2-3x vs FP16
AWQ4Activation-aware~97%3-4x vs FP16
GPTQ4One-shot~95%3-4x vs FP16

Hands-On Labs

  1. Lab 4.1: Convert Llama-7B to TensorRT-LLM, benchmark latency
  2. Lab 4.2: Apply INT8 quantization, measure accuracy on MMLU
  3. Lab 4.3: Implement AWQ on Mistral-7B, compare with GPTQ

Week 4 Checkpoint

Week 4 Completion Checklist

0/6 completed

Week 5: Fine-Tuning with PEFT (Days 29-35)

Goal: Master parameter-efficient fine-tuning techniques for production use.

Daily Schedule

DayTopicActivityHours
Day 29Full fine-tuning baselineFine-tune GPT-2 on custom dataset3.0
Day 30LoRA fundamentalsImplement LoRA from scratch, understand math3.0
Day 31LoRA hyperparametersExperiment with rank, alpha, target modules3.0
Day 32QLoRAFine-tune Llama-7B on single GPU with QLoRA3.0
Day 33Data preparationCreate instruction-tuning dataset, quality filtering2.5
Day 34Merging adaptersMerge LoRA weights, compare with base model2.5
Day 35Week 5 ReviewComplete Domain 2 practice exam3.0

LoRA Configuration Guide

Model SizeRecommended RankAlphaTarget Modules
1-3B8-1616-32q_proj, v_proj
7-13B16-3232-64q_proj, v_proj, k_proj, o_proj
30-70B32-6464-128All attention + MLP

Hands-On Labs

  1. Lab 5.1: Fine-tune Mistral-7B with LoRA on a classification task
  2. Lab 5.2: Implement QLoRA with 4-bit base model
  3. Lab 5.3: Create and clean an instruction-tuning dataset

Week 5 Checkpoint

Week 5 Completion Checklist

0/6 completed

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Week 6: Deployment with Triton (Days 36-42)

Goal: Deploy production-ready LLM inference services.

Daily Schedule

DayTopicActivityHours
Day 36Triton basicsDeploy simple model, understand config.pbtxt3.0
Day 37LLM deploymentConfigure Triton for transformer models3.0
Day 38Dynamic batchingEnable and tune batching parameters2.5
Day 39Ensemble modelsBuild preprocessing + model + postprocessing pipeline2.5
Day 40MonitoringSet up Prometheus + Grafana for Triton metrics2.5
Day 41Auto-scalingConfigure Kubernetes HPA for LLM workloads2.5
Day 42Week 6 ReviewComplete Domain 4 practice exam2.0

Triton Configuration for LLMs

name: "llm_model"
backend: "tensorrtllm"
max_batch_size: 8

dynamic_batching {
  max_queue_delay_microseconds: 100000
  preferred_batch_size: [ 1, 4, 8 ]
}

instance_group [
  {
    count: 1
    kind: KIND_GPU
    gpus: [ 0 ]
  }
]

Hands-On Labs

  1. Lab 6.1: Deploy quantized model with Triton Inference Server
  2. Lab 6.2: Configure dynamic batching, measure throughput
  3. Lab 6.3: Set up end-to-end monitoring dashboard

Week 6 Checkpoint

Week 6 Completion Checklist

0/6 completed

Week 7: Evaluation & Responsible AI (Days 43-49)

Goal: Master evaluation methodologies and responsible AI practices.

Daily Schedule

DayTopicActivityHours
Day 43Evaluation metricsImplement BLEU, ROUGE, BERTScore from scratch2.5
Day 44BenchmarksRun MMLU, HellaSwag on fine-tuned model2.5
Day 45Human evaluationDesign and conduct human eval experiment2.0
Day 46Bias detectionTest model for demographic bias2.0
Day 47GuardrailsImplement NeMo Guardrails for safety2.5
Day 48Red teamingConduct adversarial testing session2.0
Day 49Week 7 ReviewComplete Domain 5 practice exam1.5

Evaluation Metrics Cheat Sheet

MetricFormula EssenceBest For
BLEUPrecision of n-gram overlapTranslation
ROUGE-LLongest common subsequenceSummarization
BERTScoreSemantic similarity via embeddingsParaphrase
PerplexityGeometric mean of 1/probabilityLanguage modeling

Hands-On Labs

  1. Lab 7.1: Evaluate model on MMLU, report per-category scores
  2. Lab 7.2: Implement bias testing across demographic groups
  3. Lab 7.3: Build guardrails system preventing topic drift

Week 7 Checkpoint

Week 7 Completion Checklist

0/6 completed

Week 8: Practice Exams & Final Review (Days 50-56)

Goal: Achieve consistent 80%+ scores on practice exams and solidify weak areas.

Final Week Strategy

Your goal this week is NOT to learn new material. Focus entirely on:

  1. Taking full-length practice exams
  2. Reviewing wrong answers deeply
  3. Reinforcing weak areas
  4. Building exam-day confidence

Daily Schedule

DayActivityTarget ScoreHours
Day 50Practice Exam 1 (Full)70%+2.5
Day 51Review wrong answers, study gapsN/A2.0
Day 52Practice Exam 2 (Full)75%+2.5
Day 53Deep dive into weakest domainN/A2.0
Day 54Practice Exam 3 (Full)80%+2.5
Day 55Final review, flashcardsN/A1.0
Day 56EXAM DAYPASS!—

Practice Exam Strategy

Week 8 Checkpoint

Week 8 Final Checklist

0/8 completed

Exam Day Preparation

The Night Before

Exam Morning

During the Exam

Resources Summary

Official NVIDIA Resources

Preporato Resources

Community Resources

Success Metrics

Score Progression Target

MilestoneTarget ScoreActual Score
Week 2 Practice60%___%
Week 4 Practice70%___%
Week 6 Practice75%___%
Final Practice (Week 8)80%+___%

Study Hours Tracking

WeekTarget HoursActual Hours
Week 115___
Week 215___
Week 318___
Week 420___
Week 520___
Week 618___
Week 715___
Week 812___
Total133___

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Last updated: February 2026. Study plan based on NVIDIA certification requirements and successful candidate feedback.

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