NVIDIA Certified Associate: Generative AI LLMs Study Guide

How the Exam Is Structured

NVIDIA Certified Associate: Generative AI LLMs (NCA-GENL) validates generative ai with llms and prompting, core machine learning, ai, and transformer foundations, nvidia tools, performance, and deployment, evaluation, experimentation, and data analysis. The ExamPal practice bank includes 82 premium questions and 40 free questions mapped across the official blueprint.

25% of exam

Domain 2: Core Machine Learning, AI, and Transformer Foundations

Covers core machine learning concepts, evaluation metrics, transformer architecture fundamentals, key transformer components, and experimentation/data-oriented AI workflows. This domain provides the foundational ML and transformer knowledge needed to understand and assess generative AI systems.

10% of exam

Domain 4: Evaluation, Experimentation, and Data Analysis

Covers evaluation of generative AI systems, experiment design, and data analysis for model improvement. This domain emphasizes both automated and human evaluation, controlled experimentation, and using feedback and monitoring to improve outcomes.

Key Terms to Know

These terms are loaded from the shared terminology pack and appear across the question explanations.

Attention mask

A masking mechanism that prevents attention to padding tokens or disallowed positions such as future tokens in causal models.

BERT

A bidirectional transformer-based language model that uses token, position, and segment information for NLP tasks.

BLEU

A machine translation evaluation metric based on n-gram overlap between generated text and reference text.

Catastrophic forgetting

The loss of previously learned knowledge when a model is fine-tuned on new task-specific data.

Distributed training

Training a model across multiple devices or nodes to accelerate computation and scale to larger workloads.

Dropout

A regularization technique that randomly sets some neuron outputs to zero during training to reduce overfitting.

Exploratory Data Analysis (EDA)

Initial analysis of a dataset to uncover patterns, anomalies, quality issues, class imbalance, and feature relationships before model training or fine-tuning.

Feed-forward network

The position-wise fully connected sublayer in each transformer block that applies nonlinear transformations to token representations.

Fine-tuning

Adapting a pre-trained language model to a specific downstream task or application using task-specific data.

Gradient accumulation

A technique that sums gradients across multiple mini-batches before performing a weight update to simulate larger batch sizes.

Inference optimization

Techniques and tools used to improve model serving efficiency, latency, throughput, and resource usage during prediction.

Internal covariate shift

Changes in the distribution of layer inputs during training that can make optimization less stable.

Knowledge distillation

A model compression technique where a smaller student model learns to mimic a larger teacher model.

Latency

The time delay between submitting a request to a model and receiving the response.

Layer normalization

A normalization method that stabilizes training by normalizing activations within a layer across features.

Learning rate scheduling

The process of adjusting the learning rate over time to improve optimization and training stability.

Mini-batch

A small subset of training data processed in one forward and backward pass during optimization.

Model portability

The ability to move and run a model across different tools, frameworks, or deployment environments.