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Title: NCA-GENL Valid Test Bootcamp - Valid Test NCA-GENL Format
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NVIDIA NCA-GENL Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 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
  • 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
  • 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.

NVIDIA Generative AI LLMs Sample Questions (Q74-Q79):NEW QUESTION # 74
What is the correct order of steps in an ML project?
Answer: A
Explanation:
The correct order of steps in a machine learning (ML) project, as outlined in NVIDIA's Generative AI and LLMs course, is: Data collection, Data preprocessing, Model training, and Model evaluation. Data collection involves gathering relevant data for the task. Data preprocessing prepares the data by cleaning, transforming, and formatting it (e.g., tokenization for NLP). Model training involves using the preprocessed data to optimize the model's parameters. Model evaluation assesses the trained model's performance using metrics like accuracy or F1-score. This sequence ensures a systematic approach to building effective ML models.
Options A, B, and C are incorrect, as they disrupt this logical flow (e.g., evaluating before training or preprocessing before collecting data is not feasible). The course states: "An ML project follows a structured pipeline: data collection, data preprocessing, model training, and model evaluation, ensuring data is properly prepared and models are rigorously assessed." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 75
Which feature of the HuggingFace Transformers library makes it particularly suitable for fine-tuning large language models on NVIDIA GPUs?
Answer: C
Explanation:
The HuggingFace Transformers library is widely used for fine-tuning large language models (LLMs) due to its seamless integration with PyTorch and NVIDIA's TensorRT, enabling GPU-accelerated training and inference. NVIDIA's NeMo documentation references HuggingFace Transformers for its compatibility with CUDA and TensorRT, which optimize model performance on NVIDIA GPUs through features like mixed- precision training and dynamic shape inference. This makes it ideal for scaling LLM fine-tuning on GPU clusters. Option A is incorrect, as Transformers focuses on GPU, not CPU, pipelines. Option C is partially true but not the primary feature for fine-tuning. Option D is false, as Transformers is for deep learning, not classical algorithms.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplear ... /docs/en/stable/nlp
/intro.html
HuggingFace Transformers Documentation: https://huggingface.co/docs/transformers/index

NEW QUESTION # 76
When designing prompts for a large language model to perform a complex reasoning task, such as solving a multi-step mathematical problem, which advanced prompt engineering technique is most effective in ensuring robust performance across diverse inputs?
Answer: A
Explanation:
Chain-of-thought (CoT) prompting is an advanced prompt engineering technique that significantly enhances a large language model's (LLM) performance on complex reasoning tasks, such as multi-step mathematical problems. By including examples that explicitly demonstrate step-by-step reasoning in the prompt, CoT guides the model to break down the problem into intermediate steps, improving accuracy and robustness.
NVIDIA's NeMo documentation on prompt engineering highlights CoT as a powerful method for tasks requiring logical or sequential reasoning, as it leverages the model's ability to mimic structured problem- solving. Research by Wei et al. (2022) demonstrates that CoT outperforms other methods for mathematical reasoning. Option A (zero-shot) is less effective for complex tasks due to lack of guidance. Option B (few- shot with random examples) is suboptimal without structured reasoning. Option D (RAG) is useful for factual queries but less relevant for pure reasoning tasks.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplear ... able/nlp/intro.html Wei, J., et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models."

NEW QUESTION # 77
What do we usually refer to as generative AI?
Answer: B
Explanation:
Generative AI, as covered in NVIDIA's Generative AI and LLMs course, is a branch of artificial intelligence focused on creating models that can generate new and original data, such as text, images, or audio, that resembles the training data. In the context of LLMs, generative AI involves models like GPT that produce coherent text for tasks like text completion, dialogue, or creative writing by learning patterns from large datasets. These models use techniques like autoregressive generation to create novel outputs. Option B is incorrect, as generative AI is not limited to generating classification models but focuses on producing new data. Option C is wrong, as improving model efficiency is a concern of optimization techniques, not generative AI. Option D is inaccurate, as analyzing and interpreting data falls under discriminative AI, not generative AI. The course emphasizes: "Generative AI involves building models that create new content, such as text or images, by learning the underlying distribution of the training data." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 78
What are the main advantages of instructed large language models over traditional, small language models (<
300M parameters)? (Pick the 2 correct responses)
Answer: C,E
Explanation:
Instructed large language models (LLMs), such as those supported by NVIDIA's NeMo framework, have significant advantages over smaller, traditional models:
* Option D: LLMs often have cheaper computational costs during inference for certain tasks because they can generalize across multiple tasks without requiring task-specific retraining, unlike smaller models that may need separate models per task.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplear ... /docs/en/stable/nlp
/intro.html
Brown, T., et al. (2020). "Language Models are Few-Shot Learners."

NEW QUESTION # 79
......
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