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NVIDIA NCA-GENL Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

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 5

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 6

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 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 (Q19-Q24):

NEW QUESTION # 19
Which of the following prompt engineering techniques is most effective for improving an LLM's performance on multi-step reasoning tasks?

A. Retrieval-augmented generation without context
B. Zero-shot prompting with detailed task descriptions.
C. Few-shot prompting with unrelated examples.
D. Chain-of-thought prompting with explicit intermediate steps.

Answer: D

Explanation:
Chain-of-thought (CoT) prompting is a highly effective technique for improving large language model (LLM) performance on multi-step reasoning tasks. By including explicit intermediate steps in the prompt, CoT guides the model to break down complex problems into manageable parts, improving reasoning accuracy. NVIDIA's NeMo documentation on prompt engineering highlights CoT as a powerful method for tasks like mathematical reasoning or logical problem-solving, as it leverages the model's ability to follow structured reasoning paths. Option A is incorrect, as retrieval-augmented generation (RAG) without context is less effective for reasoning tasks. Option B is wrong, as unrelated examples in few-shot prompting do not aid reasoning. Option C (zero-shot prompting) is less effective than CoT for complex reasoning.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html Wei, J., et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models."

NEW QUESTION # 20
What is the primary purpose of applying various image transformation techniques (e.g., flipping, rotation, zooming) to a dataset?

A. To ensure perfect alignment and uniformity across all images in the dataset.
B. To simplify the model's architecture, making it easier to interpret the results.
C. To artificially expand the dataset's size and improve the model's ability to generalize.
D. To reduce the computational resources required for training deep learning models.

Answer: C

Explanation:
Image transformation techniques such as flipping, rotation, and zooming are forms of data augmentation used to artificially increase the size and diversity of a dataset. NVIDIA's Deep Learning AI documentation, particularly for computer vision tasks using frameworks like DALI (Data Loading Library), explains that data augmentation improves a model's ability to generalize by exposing it to varied versions of the training data, thus reducing overfitting. For example, flipping an image horizontally creates a new training sample that helps the model learn invariance to certain transformations. Option A is incorrect because transformations do not simplify the model architecture. Option C is wrong, as augmentation introduces variability, not uniformity. Option D is also incorrect, as augmentation typically increases computational requirements due to additional data processing.
References:
NVIDIA DALI Documentation: https://docs.nvidia.com/deeplearning/dali/user-guide/docs/index.html

NEW QUESTION # 21
In the context of language models, what does an autoregressive model predict?

A. The probability of the next token by looking at the previous and future input tokens.
B. The next token solely using recurrent network or LSTM cells.
C. The probability of the next token using a Monte Carlo sampling of past tokens.
D. The probability of the next token in a text given the previous tokens.

Answer: D

Explanation:
Autoregressive models are a cornerstone of modern language modeling, particularly in large language models (LLMs) like those discussed in NVIDIA's Generative AI and LLMs course. These models predict the probability of the next token in a sequence based solely on the preceding tokens, making them inherently sequential and unidirectional. This process is often referred to as "next-token prediction," where the model learns to generate text by estimating the conditional probability distribution of the next token given the context of all previous tokens. For example, given the sequence "The cat is," the model predicts the likelihood of the next word being "on," "in," or another token. This approach is fundamental to models like GPT, which rely on autoregressive decoding to generate coherent text. Unlike bidirectional models (e.g., BERT), which consider both previous and future tokens, autoregressive models focus only on past tokens, making option D incorrect. Options B and C are also inaccurate, as Monte Carlo sampling is not a standard method for next- token prediction in autoregressive models, and the prediction is not limited to recurrent networks or LSTM cells, as modern LLMs often use Transformer architectures. The course emphasizes this concept in the context of Transformer-based NLP: "Learn the basic concepts behind autoregressive generative models, including next-token prediction and its implementation within Transformer-based models." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 22
Which technology will allow you to deploy an LLM for production application?

A. Triton
B. Git
C. Falcon
D. Pandas

Answer: A

Explanation:
NVIDIA Triton Inference Server is a technology specifically designed for deploying machine learning models, including large language models (LLMs), in production environments. It supports high-performance inference, model management, and scalability across GPUs, making it ideal for real-time LLM applications.
According to NVIDIA's Triton Inference Server documentation, it supports frameworks like PyTorch and TensorFlow, enabling efficient deployment of LLMs with features like dynamic batching and model ensemble. Option A (Git) is a version control system, not a deployment tool. Option B (Pandas) is a data analysis library, irrelevant to model deployment. Option C (Falcon) refers to a specific LLM, not a deployment platform.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html

NEW QUESTION # 23
When should one use data clustering and visualization techniques such as tSNE or UMAP?

A. When there is a need to perform regression analysis and predict continuous numerical values.
B. When there is a need to perform feature extraction and identify important variables in the dataset.
C. When there is a need to handle missing values and impute them in the dataset.
D. When there is a need to reduce the dimensionality of the data and visualize the clusters in a lower- dimensional space.

Answer: D

Explanation:
Data clustering and visualization techniques like t-SNE (t-Distributed Stochastic Neighbor Embedding) and UMAP (Uniform Manifold Approximation and Projection) are used to reduce the dimensionality of high- dimensional datasets and visualize clusters in a lower-dimensional space, typically 2D or 30 for interpretation.
As covered in NVIDIA's Generative AI and LLMs course, these techniques are particularly valuable in exploratory data analysis (EDA) for identifying patterns, groupings, or structure in data, such as clustering similar text embeddings in NLP tasks. They help reveal underlying relationships in complex datasets without requiring labeled data. Option A is incorrect, as t-SNE and UMAP are not designed for handling missing values, which is addressed by imputation techniques. Option B is wrong, as these methods are not used for regression analysis but for unsupervised visualization. Option D is inaccurate, as feature extraction is typically handled by methods like PCA or autoencoders, not t-SNE or UMAP, which focus on visualization. The course notes: "Techniques like t-SNE and UMAP are used to reduce data dimensionality and visualize clusters in lower-dimensional spaces, aiding in the understanding of data structure in NLP and other tasks." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 24
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