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NVIDIA NCA-GENL Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

Prompt Engineering: This section of the exam measures the skills of Prompt Designers and covers how to craft effective prompts that guide LLMs to produce desired outputs. It focuses on prompt strategies, formatting, and iterative refinement techniques used in both development and real-world applications of LLMs.

Topic 4

Software Development: This section of the exam measures the skills of Machine Learning Developers and covers writing efficient, modular, and scalable code for AI applications. It includes software engineering principles, version control, testing, and documentation practices relevant to LLM-based development.

Topic 5

Experiment Design

Topic 6

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 7

Fundamentals of Machine Learning and Neural Networks: This section of the exam measures the skills of AI Researchers and covers the foundational principles behind machine learning and neural networks, focusing on how these concepts underpin the development of large language models (LLMs). It ensures the learner understands the basic structure and learning mechanisms involved in training generative AI systems.

Topic 8

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 9

Alignment: This section of the exam measures the skills of AI Policy Engineers and covers techniques to align LLM outputs with human intentions and values. It includes safety mechanisms, ethical safeguards, and tuning strategies to reduce harmful, biased, or inaccurate results from models.

Topic 10

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.

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NVIDIA Generative AI LLMs Sample Questions (Q50-Q55):

NEW QUESTION # 50
Why do we need positional encoding in transformer-based models?

A. To reduce the dimensionality of the input data.
B. To represent the order of elements in a sequence.
C. To increase the throughput of the model.
D. To prevent overfitting of the model.

Answer: B

Explanation:
Positional encoding is a critical component in transformer-based models because, unlike recurrent neural networks (RNNs), transformers process input sequences in parallel and lack an inherent sense of word order.
Positional encoding addresses this by embedding information about the position of each token in the sequence, enabling the model to understand the sequential relationships between tokens. According to the original transformer paper ("Attention is All You Need" by Vaswani et al., 2017), positional encodings are added to the input embeddings to provide the model with information about the relative or absolute position of tokens. NVIDIA's documentation on transformer-based models, such as those supported by the NeMo framework, emphasizes that positional encodings are typically implemented using sinusoidal functions or learned embeddings to preserve sequence order, which is essential for tasks like natural language processing (NLP). Options B, C, and D are incorrect because positional encoding does not address overfitting, dimensionality reduction, or throughput directly; these are handled by other techniques like regularization, dimensionality reduction methods, or hardware optimization.
References:
Vaswani, A., et al. (2017). "Attention is All You Need."
NVIDIA NeMo Documentation:https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html

NEW QUESTION # 51
In the context of machine learning model deployment, how can Docker be utilized to enhance the process?

A. To reduce the computational resources needed for training models.
B. To automatically generate features for machine learning models.
C. To provide a consistent environment for model training and inference.
D. To directly increase the accuracy of machine learning models.

Answer: C

Explanation:
Docker is a containerization platform that ensures consistent environments for machine learning model training and inference by packaging dependencies, libraries, and configurations into portable containers.
NVIDIA's documentation on deploying models with Triton Inference Server and NGC (NVIDIA GPU Cloud) emphasizes Docker's role in eliminating environment discrepancies between development and production, ensuring reproducibility. Option A is incorrect, as Docker does not generate features. Option C is false, as Docker does not reduce computational requirements. Option D is wrong, as Docker does not affect model accuracy.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html
NVIDIA NGC Documentation: https://docs.nvidia.com/ngc/ngc-overview/index.html

NEW QUESTION # 52
In the development of trustworthy AI systems, what is the primary purpose of implementing red-teaming exercises during the alignment process of large language models?

A. To optimize the model's inference speed for production deployment.
B. To automate the collection of training data for fine-tuning.
C. To increase the model's parameter count for better performance.
D. To identify and mitigate potential biases, safety risks, and harmful outputs.

Answer: D

Explanation:
Red-teaming exercises involve systematically testing a large language model (LLM) by probing it with adversarial or challenging inputs to uncover vulnerabilities, such as biases, unsafe responses, or harmful outputs. NVIDIA's Trustworthy AI framework emphasizes red-teaming as a critical stepin the alignment process to ensure LLMs adhere to ethical standards and societal values. By simulating worst-case scenarios, red-teaming helps developers identify and mitigate risks, such as generating toxic content or reinforcing stereotypes, before deployment. Option A is incorrect, as red-teaming focuses on safety, not speed. Option C is false, as it does not involve model size. Option D is wrong, as red-teaming is about evaluation, not data collection.
References:
NVIDIA Trustworthy AI: https://www.nvidia.com/en-us/ai-data-science/trustworthy-ai/

NEW QUESTION # 53
Which of the following optimizations are provided by TensorRT? (Choose two.)

A. Layer Fusion
B. Data augmentation
C. Variable learning rate
D. Residual connections
E. Multi-Stream Execution

Answer: A,E

Explanation:
NVIDIA TensorRT provides optimizations to enhance the performance of deep learning models during inference, as detailed in NVIDIA's Generative AI and LLMs course. Two key optimizations are multi-stream execution and layer fusion. Multi-stream execution allows parallel processing of multiple input streams on the GPU, improving throughput for concurrent inference tasks. Layer fusion combines multiple layers of a neural network (e.g., convolution and activation) into a single operation, reducing memory access and computation time. Option A, data augmentation, is incorrect, as it is a preprocessing technique, not a TensorRT optimization. Option B, variable learning rate, is a training technique, not relevant to inference. Option E, residual connections, is a model architecture feature, not a TensorRT optimization. The course states:
"TensorRT optimizes inference through techniques like layer fusion, which combines operations to reduce overhead, and multi-stream execution, which enables parallel processing for higher throughput." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 54
What is Retrieval Augmented Generation (RAG)?

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

Answer: A

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. OptionA 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 # 55
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