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NVIDIA NCA-GENL Exam Syllabus Topics:

Topic
Details

Topic 1

Data Preprocessing and Feature Engineering: This section of the exam measures the skills of Data Engineers and covers preparing raw data into usable formats for model training or fine-tuning. It includes cleaning, normalizing, tokenizing, and feature extraction methods essential to building robust LLM pipelines.

Topic 2

Experimentation: This section of the exam measures the skills of ML Engineers and covers how to conduct structured experiments with LLMs. It involves setting up test cases, tracking performance metrics, and making informed decisions based on experimental outcomes.:

Topic 3

Python Libraries for LLMs: This section of the exam measures skills of LLM Developers and covers using Python tools and frameworks like Hugging Face Transformers, LangChain, and PyTorch to build, fine-tune, and deploy large language models. It focuses on practical implementation and ecosystem familiarity.

Topic 4

This section of the exam measures skills of AI Product Developers and covers how to strategically plan experiments that validate hypotheses, compare model variations, or test model responses. It focuses on structure, controls, and variables in experimentation.

Topic 5

LLM Integration and Deployment: This section of the exam measures skills of AI Platform Engineers and covers connecting LLMs with applications or services through APIs, and deploying them securely and efficiently at scale. It also includes considerations for latency, cost, monitoring, and updates in production environments.

Topic 6

Experiment Design

Topic 7

Data Analysis and Visualization: This section of the exam measures the skills of Data Scientists and covers interpreting, cleaning, and presenting data through visual storytelling. It emphasizes how to use visualization to extract insights and evaluate model behavior, performance, or training data patterns.

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NVIDIA Generative AI LLMs Sample Questions (Q49-Q54):

NEW QUESTION # 49
You are working on developing an application to classify images of animals and need to train a neural model.
However, you have a limited amount of labeled data. Which technique can you use to leverage the knowledge from a model pre-trained on a different task to improve the performance of your new model?

A. Random initialization
B. Transfer learning
C. Early stopping
D. Dropout

Answer: B

Explanation:
Transfer learning is a technique where a model pre-trained on a large, general dataset (e.g., ImageNet for computer vision) is fine-tuned for a specific task with limited data. NVIDIA's Deep Learning AI documentation, particularly for frameworks like NeMo and TensorRT, emphasizes transfer learning as a powerful approach to improve model performance when labeled data is scarce. For example, a pre-trained convolutional neural network (CNN) can be fine-tuned for animal image classification by reusing its learned features (e.g., edge detection) and adapting the final layers to the new task. Option A (dropout) is a regularization technique, not a knowledge transfer method. Option B (random initialization) discards pre- trained knowledge. Option D (early stopping) prevents overfitting but does not leverage pre-trained models.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/model_finetuning.html
NVIDIA Deep Learning AI:https://www.nvidia.com/en-us/deep-learning-ai/

NEW QUESTION # 50
What is Retrieval Augmented Generation (RAG)?

A. RAG is a method for manipulating and generating text-based data using Transformer-based LLMs.
B. RAG is a methodology that combines an information retrieval component with a response generator.
C. RAG is an architecture used to optimize the output of an LLM by retraining the model with domain- specific data.
D. RAG is a technique used to fine-tune pre-trained LLMs for improved performance.

Answer: B

Explanation:
Retrieval-Augmented Generation (RAG) is a methodology that enhances the performance of large language models (LLMs) by integrating an information retrieval component with a generative model. As described in the seminal paper by Lewis et al. (2020), RAG retrieves relevant documents from an external knowledge base (e.g., using dense vector representations) and uses them to inform the generative process, enabling more accurate and contextually relevant responses. NVIDIA's documentation on generative AI workflows, particularly in the context of NeMo and Triton Inference Server, highlights RAG as a technique to improve LLM outputs by grounding them in external data, especially for tasks requiring factual accuracy or domain- specific knowledge. Option A is incorrect because RAG does not involve retraining the model but rather augments it with retrieved data. Option C is too vague and does not capture the retrieval aspect, while Option D refers to fine-tuning, which is a separate process.
References:
Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html

NEW QUESTION # 51
Which model deployment framework is used to deploy an NLP project, especially for high-performance inference in production environments?

A. HuggingFace
B. NeMo
C. NVIDIA Triton
D. NVIDIA DeepStream

Answer: C

Explanation:
NVIDIA Triton Inference Server is a high-performance framework designed for deploying machine learning models, including NLP models, in production environments. It supports optimized inference on GPUs, dynamic batching, and integration with frameworks like PyTorch and TensorFlow. According to NVIDIA's Triton documentation, it is ideal for deploying LLMs for real-time applications with low latency. Option A (DeepStream) is for video analytics, not NLP. Option B (HuggingFace) is a library for model development, not deployment. Option C (NeMo) is for training and fine-tuning, not production deployment.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html

NEW QUESTION # 52
Which of the following is a key characteristic of Rapid Application Development (RAD)?

A. Minimal user feedback during the development process.
B. Linear progression through predefined project phases.
C. Extensive upfront planning before any development.
D. Iterative prototyping with active user involvement.

Answer: D

Explanation:
Rapid Application Development (RAD) is a software development methodology that emphasizes iterative prototyping and active user involvement to accelerate development and ensure alignment with user needs.
NVIDIA's documentation on AI application development, particularly in the context of NGC (NVIDIA GPU Cloud) and software workflows, aligns with RAD principles for quickly building and iterating on AI-driven applications. RAD involves creating prototypes, gathering user feedback, and refining the application iteratively, unlike traditional waterfall models. Option B is incorrect, as RAD minimizes upfront planning in favor of flexibility. Option C describes a linear waterfall approach, not RAD. Option D is false, as RAD relies heavily on user feedback.
References:
NVIDIA NGC Documentation: https://docs.nvidia.com/ngc/ngc-overview/index.html

NEW QUESTION # 53
Which metric is commonly used to evaluate machine-translation models?

A. F1 Score
B. BLEU score
C. ROUGE score
D. Perplexity

Answer: B

Explanation:
The BLEU (Bilingual Evaluation Understudy) score is the most commonly used metric for evaluating machine-translation models. It measures the precision of n-gram overlaps between the generated translation and reference translations, providing a quantitative measure of translation quality. NVIDIA's NeMo documentation on NLP tasks, particularly machine translation, highlights BLEU as the standard metric for assessing translation performance due to its focus on precision and fluency. Option A (F1 Score) is used for classification tasks, not translation. Option C (ROUGE) is primarily for summarization, focusing on recall.
Option D (Perplexity) measures language model quality but is less specific to translation evaluation.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
Papineni, K., et al. (2002). "BLEU: A Method for Automatic Evaluation of Machine Translation."

NEW QUESTION # 54
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