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[General] NCA-GENL Latest Dumps Ebook | Valid NCA-GENL Test Preparation

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【General】 NCA-GENL Latest Dumps Ebook | Valid NCA-GENL Test Preparation

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NVIDIA NCA-GENL Exam Syllabus Topics:
TopicDetails
Topic 1
  • Experiment Design
Topic 2
  • 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.
Topic 3
  • 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 4
  • 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 5
  • 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 6
  • 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.

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NVIDIA Generative AI LLMs Sample Questions (Q77-Q82):NEW QUESTION # 77
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?
  • A. Retrieval-augmented generation with external mathematical databases.
  • B. Chain-of-thought prompting with step-by-step reasoning examples.
  • C. Zero-shot prompting with a generic task description.
  • D. Few-shot prompting with randomly selected examples.
Answer: B
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 # 78
You have developed a deep learning model for a recommendation system. You want to evaluate the performance of the model using A/B testing. What is the rationale for using A/B testing with deep learning model performance?
  • A. A/B testing ensures that the deep learning model is robust and can handle different variations of input data.
  • B. A/B testing allows for a controlled comparison between two versions of the model, helping to identify the version that performs better.
  • C. A/B testing helps in collecting comparative latency data to evaluate the performance of the deep learning model.
  • D. A/B testing methodologies integrate rationale and technical commentary from the designers of the deep learning model.
Answer: B
Explanation:
A/B testing is a controlled experimentation method used to compare two versions of a system (e.g., two model variants) to determine which performs better based on a predefined metric (e.g., user engagement, accuracy).
NVIDIA's documentation on model optimization and deployment, such as with Triton Inference Server, highlights A/B testing as a method to validate model improvements in real-world settings by comparing performance metrics statistically. For a recommendation system, A/B testing might compare click-through rates between two models. Option B is incorrect, as A/B testing focuses on outcomes, not designer commentary. Option C is misleading, as robustness is tested via other methods (e.g., stress testing). Option D is partially true but narrow, as A/B testing evaluates broader performance metrics, not just latency.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html

NEW QUESTION # 79
What do we usually refer to as generative AI?
  • A. A branch of artificial intelligence that focuses on auto generation of models for classification.
  • B. A branch of artificial intelligence that focuses on creating models that can generate new and original data.
  • C. A branch of artificial intelligence that focuses on analyzing and interpreting existing data.
  • D. A branch of artificial intelligence that focuses on improving the efficiency of existing models.
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 # 80
How can Retrieval Augmented Generation (RAG) help developers to build a trustworthy AI system?
  • A. RAG can align AI models with one another, improving the accuracy of AI systems through cross- checking.
  • B. RAG can generate responses that cite reference material from an external knowledge base, ensuring transparency and verifiability.
  • C. RAG can enhance the security features of AI systems, ensuring confidential computing and encrypted traffic.
  • D. RAG can improve the energy efficiency of AI systems, reducing their environmental impact and cooling requirements.
Answer: B
Explanation:
Retrieval-Augmented Generation (RAG) enhances trustworthy AI by generating responses that cite reference material from an external knowledge base, ensuring transparency and verifiability, as discussed in NVIDIA's Generative AI and LLMs course. RAG combines a retriever to fetch relevant documents with a generator to produce responses, allowing outputs to be grounded in verifiable sources, reducing hallucinations and improving trust. Option A is incorrect, as RAG does not focus on security features like confidential computing. Option B is wrong, as RAG is unrelated to energy efficiency. Option C is inaccurate, as RAG does not align models but integrates retrieved knowledge. The course notes: "RAG enhances trustworthy AI by generating responses with citations from external knowledge bases, improving transparency and verifiability of outputs." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 81
Your company has upgraded from a legacy LLM model to a new model that allows for larger sequences and higher token limits. What is the most likely result of upgrading to the new model?
  • A. The newer model allows larger context, so outputs will improve, but you will likely incur longer inference times.
  • B. The newer model allows for larger context, so the outputs will improve without increasing inference time overhead.
  • C. The newer model allows the same context lengths, but the larger token limit will result in more comprehensive and longer outputs with more detail.
  • D. The number of tokens is fixed for all existing language models, so there is no benefit to upgrading to higher token limits.
Answer: A
Explanation:
Upgrading to a new LLM with larger sequence lengths and higher token limits, as discussed in NVIDIA's Generative AI and LLMs course, typically allows the model to process larger contexts, leading to improved output quality due to better understanding of extended dependencies in text. However, handling larger sequences increases computational requirements, often resulting in longer inference times, especially on the same hardware. This trade-off is a key consideration in LLM deployment. Option A is incorrect, as token limits vary across models, and higher limits offer benefits. Option B is wrong, as larger context processing typically increases inference time. Option C is inaccurate, as higher token limits primarily enable larger context, not just longer outputs. The course notes: "Larger sequence lengths in LLMs allow for improved output quality by capturing more context, but this often comes at the cost of increased inference times due to higher computational demands." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 82
......
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