Applied AI cert-prep add-on

NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) Exam Prep Add-On

Convert AI Engineering Mastery into an NCA-GENL ramp focused on the NVIDIA LLM serving stack.

$147 add-on45h additional studyTarget exam: NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL)Exam fee: $135

What the add-on ships

++Domain reviews aligned to the official NVIDIA NCA-GENL exam outline
++Three 50-question mock exams matching the NVIDIA exam format and difficulty
++Focused review of NeMo, Triton Inference Server, TensorRT-LLM, and the NVIDIA NIM microservice line
++Prompt engineering and RAG patterns specifically tested on NCA-GENL
++Direct link to the official NVIDIA certification page as the canonical reference

Built on the parent course

++AI Engineering Mastery 18-module curriculum covering RAG, agents, observability, and cost engineering
++LLM serving and inference optimization material from the parent course that maps directly to NVIDIA exam scenarios

Parent course: ai engineering mastery

Best for

++AI Engineers and ML Engineers serving production LLMs on NVIDIA hardware
++AI Infrastructure Engineers managing GPU clusters and LLM serving operations
++Generative AI Engineers seeking a vendor credential aligned to inference-optimization work

Practice surface

++Three 50-question mock exams in NVIDIA's question format
++Hands-on familiarization scripts for NeMo, Triton, and TensorRT-LLM (runnable on a single GPU)
++Targeted weak-area question drilling per NCA-GENL objective

Buy the add-on

$147 on top of the ai engineering mastery parent course. Lifetime access to the practice materials, mock exams, and exam-day worksheets.

See the parent course

Exam summary

NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) is NVIDIA's practitioner-level credential focused on production LLM serving on the NVIDIA software stack. The exam covers LLM fundamentals, RAG and prompt patterns, fine-tuning, the NVIDIA inference stack (NeMo, Triton Inference Server, TensorRT-LLM, NVIDIA NIM), and operational considerations including responsible AI. Exam is 60 minutes online proctored.

Domain reviews

LLM Fundamentals

Foundational concepts: tokenization, transformer architecture, attention, autoregressive decoding.

++Tokenization, byte-pair encoding, vocabulary size implications
++Transformer architecture: attention, feed-forward, residual, layer norm
++Autoregressive decoding: greedy, beam search, top-k, top-p (nucleus), temperature
++Context window, position embeddings (RoPE, ALiBi)
++Model sizes and capability tiers, scaling laws

Primary sources:

Prompt Engineering and RAG

How to specialize a model at inference time without training.

++Prompt patterns: zero-shot, few-shot, chain-of-thought, in-context learning
++Retrieval-Augmented Generation (Lewis et al. 2020): retriever, generator, fusion
++Embedding models, vector databases, hybrid retrieval
++Reranking, query rewriting, context packing
++Evaluation: retrieval recall, generation grounding, hallucination rate

Primary sources:

Fine-Tuning and Customization

How to specialize a base model with new training signal.

++Full fine-tuning vs PEFT (LoRA, QLoRA, prefix tuning)
++Instruction tuning, alignment training (RLHF, DPO, constitutional AI)
++Data preparation: instruction-response pairs, preference rankings, quality control
++Training infrastructure: distributed training, mixed precision, gradient checkpointing
++Evaluation: capability evaluation, safety evaluation, regression testing

Primary sources:

NVIDIA Inference Stack

Production serving on NVIDIA hardware and software.

++NeMo framework: pretraining, fine-tuning, deployment
++Triton Inference Server: model serving, batching, multi-model deployments
++TensorRT-LLM: inference optimization, kernel fusion, in-flight batching
++NVIDIA NIM (NVIDIA Inference Microservices): containerized model endpoints
++Quantization techniques: FP16, FP8, INT8, INT4 with calibration

Primary sources:

Operational Considerations and Responsible AI

Production-grade concerns beyond model accuracy.

++Latency, throughput (tokens-per-second), batching strategies
++KV cache management, PagedAttention
++Cost: dollars-per-million-tokens, GPU utilization
++Responsible AI: bias evaluation, content moderation, NeMo Guardrails
++Observability: model performance monitoring, drift detection

Primary sources:

Practice scenarios (7)

Practice scenarios are scenario-based learning, not exam-question mimicry. Each scenario maps to a specific exam domain and includes a worked explanation plus a primary-source citation. Reproducing actual exam items would violate the cert body's NDA; the format here exercises the same underlying concepts under different surface phrasing.

Preview scenario 1 of 7 · LLM Fundamentalsfree preview

A team is generating customer-service responses with an LLM and wants more consistent output for factual questions. Which sampling parameter set is most appropriate?

A. Temperature 1.5, top-p 0.95
B. Temperature 0.0 to 0.3, top-p 0.9
C. Temperature 2.0, top-k 1
D. Temperature 0.7, top-p 1.0

Answer: B

Factual customer-service responses benefit from low temperature (closer to greedy decoding) to reduce variance, paired with a moderate top-p (0.9) to allow some flexibility on phrasing. High temperature increases creativity but reduces factual consistency. The other options either combine high temperature with low top-k (nonsensical) or set top-p to 1.0 (effectively disabling nucleus sampling).

Reference: Holtzman et al.: The Curious Case of Neural Text Degeneration (ICLR 2020)

Unlock the rest

6 more practice scenarios with full explanations and primary-source citations

The remaining scenarios cover every exam domain at the same depth as the preview above. Includes the exam-day strategy guide and additional study resources. $147 one-time, lifetime access.

Exam-day strategy

++Read each question for which layer of the NVIDIA stack it tests (NeMo, Triton, TensorRT-LLM, NIM). The right answer often turns on the layer.
++For prompt-engineering questions, identify whether grounding (RAG context) or behavior (system prompt) is the issue. Distractors swap between them.
++Fine-tuning questions almost always have one feasibility-driven correct answer. Eliminate options that require infeasible hardware.
++Watch for distractors that confuse training-time techniques (gradient checkpointing, mixed precision) with serving-time techniques (in-flight batching, PagedAttention).
++Pace at roughly 70 seconds per question. 60 minutes for 50 questions is moderate; flag and return on hardware-trivia questions.

Additional resources

Sources

Official NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) exam page (certifying body)

Exam fee and blueprint last verified 2026-05-22. Confirm current values with the certifying body before scheduling the exam.

What the add-on ships

++Domain reviews aligned to the official NVIDIA NCA-GENL exam outline

++Three 50-question mock exams matching the NVIDIA exam format and difficulty

++Focused review of NeMo, Triton Inference Server, TensorRT-LLM, and the NVIDIA NIM microservice line

++Prompt engineering and RAG patterns specifically tested on NCA-GENL

++Direct link to the official NVIDIA certification page as the canonical reference

Exam summary

Domain reviews

LLM Fundamentals

Foundational concepts: tokenization, transformer architecture, attention, autoregressive decoding.

++Tokenization, byte-pair encoding, vocabulary size implications
++Transformer architecture: attention, feed-forward, residual, layer norm
++Autoregressive decoding: greedy, beam search, top-k, top-p (nucleus), temperature
++Context window, position embeddings (RoPE, ALiBi)
++Model sizes and capability tiers, scaling laws

Primary sources:

Prompt Engineering and RAG

How to specialize a model at inference time without training.

++Prompt patterns: zero-shot, few-shot, chain-of-thought, in-context learning
++Retrieval-Augmented Generation (Lewis et al. 2020): retriever, generator, fusion
++Embedding models, vector databases, hybrid retrieval
++Reranking, query rewriting, context packing
++Evaluation: retrieval recall, generation grounding, hallucination rate

Primary sources:

Fine-Tuning and Customization

How to specialize a base model with new training signal.

++Full fine-tuning vs PEFT (LoRA, QLoRA, prefix tuning)
++Instruction tuning, alignment training (RLHF, DPO, constitutional AI)
++Data preparation: instruction-response pairs, preference rankings, quality control
++Training infrastructure: distributed training, mixed precision, gradient checkpointing
++Evaluation: capability evaluation, safety evaluation, regression testing

Primary sources:

NVIDIA Inference Stack

Production serving on NVIDIA hardware and software.

++NeMo framework: pretraining, fine-tuning, deployment
++Triton Inference Server: model serving, batching, multi-model deployments
++TensorRT-LLM: inference optimization, kernel fusion, in-flight batching
++NVIDIA NIM (NVIDIA Inference Microservices): containerized model endpoints
++Quantization techniques: FP16, FP8, INT8, INT4 with calibration

Primary sources:

Operational Considerations and Responsible AI

Production-grade concerns beyond model accuracy.

++Latency, throughput (tokens-per-second), batching strategies
++KV cache management, PagedAttention
++Cost: dollars-per-million-tokens, GPU utilization
++Responsible AI: bias evaluation, content moderation, NeMo Guardrails
++Observability: model performance monitoring, drift detection

Primary sources:

Practice scenarios (7)

Preview scenario 1 of 7 · LLM Fundamentalsfree preview

A team is generating customer-service responses with an LLM and wants more consistent output for factual questions. Which sampling parameter set is most appropriate?

A. Temperature 1.5, top-p 0.95
B. Temperature 0.0 to 0.3, top-p 0.9
C. Temperature 2.0, top-k 1
D. Temperature 0.7, top-p 1.0

Answer: B

Reference: Holtzman et al.: The Curious Case of Neural Text Degeneration (ICLR 2020)

Unlock the rest

6 more practice scenarios with full explanations and primary-source citations

The remaining scenarios cover every exam domain at the same depth as the preview above. Includes the exam-day strategy guide and additional study resources. $147 one-time, lifetime access.

Exam-day strategy

++Read each question for which layer of the NVIDIA stack it tests (NeMo, Triton, TensorRT-LLM, NIM). The right answer often turns on the layer.

++For prompt-engineering questions, identify whether grounding (RAG context) or behavior (system prompt) is the issue. Distractors swap between them.

++Fine-tuning questions almost always have one feasibility-driven correct answer. Eliminate options that require infeasible hardware.

++Watch for distractors that confuse training-time techniques (gradient checkpointing, mixed precision) with serving-time techniques (in-flight batching, PagedAttention).

++Pace at roughly 70 seconds per question. 60 minutes for 50 questions is moderate; flag and return on hardware-trivia questions.