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[General] NCA-GENL Instant Discount | Real NCA-GENL Questions

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【General】 NCA-GENL Instant Discount | Real NCA-GENL Questions

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NVIDIA NCA-GENL Exam Syllabus Topics:
TopicDetails
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
  • 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 3
  • 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 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
  • 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 6
  • 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 (Q68-Q73):NEW QUESTION # 68
Which of the following principles are widely recognized for building trustworthy AI? (Choose two.)
  • A. Scalability
  • B. Privacy
  • C. Low latency
  • D. Nondiscrimination
  • E. Conversational
Answer: B,D
Explanation:
In building Trustworthy AI, privacy and nondiscrimination are widely recognized principles, as emphasized in NVIDIA's Generative AI and LLMs course. Privacy ensures that AI systems protect user data and maintain confidentiality, often through techniques like confidential computing or data anonymization.
Nondiscrimination ensures that AI models avoid biases and treat all groups fairly, mitigating issues like discriminatory outputs. Option A, conversational, is incorrect, as it is a feature of some AI systems, not a Trustworthy AI principle. Option B, low latency, is a performance goal, not a trust principle. Option D, scalability, is a technical consideration, not directly related to trustworthiness. The course states: "Trustworthy AI principles include privacy, ensuring data protection, and nondiscrimination, ensuring fair and unbiased model behavior, critical for ethical AI development." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 69
What is the fundamental role of LangChain in an LLM workflow?
  • A. To act as a replacement for traditional programming languages.
  • B. To reduce the size of AI foundation models.
  • C. To orchestrate LLM components into complex workflows.
  • D. To directly manage the hardware resources used by LLMs.
Answer: C
Explanation:
LangChain is a framework designed to simplify the development of applications powered by large language models (LLMs) by orchestrating various components, such as LLMs, external data sources, memory, and tools, into cohesive workflows. According to NVIDIA's documentation on generative AI workflows, particularly in the context of integrating LLMs with external systems, LangChain enables developers to build complex applications by chaining together prompts, retrieval systems (e.g., for RAG), and memory modules to maintain context across interactions. For example, LangChain can integrate an LLM with a vector database for retrieval-augmented generation or manage conversational history for chatbots. Option A is incorrect, as LangChain complements, not replaces, programming languages. Option B is wrong, as LangChain does not modify model size. Option D is inaccurate, as hardware management is handled by platforms like NVIDIA Triton, not LangChain.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplear ... /docs/en/stable/nlp
/intro.html
LangChain Official Documentation: https://python.langchain.com/docs/get_started/introduction

NEW QUESTION # 70
Which of the following options describes best the NeMo Guardrails platform?
  • A. Ensuring scalability and performance of large language models in pre-training and inference.
  • B. Developing and designing advanced machine learning models capable of interpreting and integrating various forms of data.
  • C. Building advanced data factories for generative AI services in the context of language models.
  • D. Ensuring the ethical use of artificial intelligence systems by monitoring and enforcing compliance with predefined rules and regulations.
Answer: D
Explanation:
The NVIDIA NeMo Guardrails platform is designed to ensure the ethical and safe use of AI systems, particularly LLMs, by enforcing predefined rules and regulations, as highlighted in NVIDIA's Generative AI and LLMs course. It provides a framework to monitor and control LLM outputs, preventing harmful or inappropriate responses and ensuring compliance with ethical guidelines. Option A is incorrect, as NeMo Guardrails focuses on safety, not scalability or performance. Option B is wrong, as it describes model development, not guardrails. Option D is inaccurate, as it does not pertain to data factories but to ethical AI enforcement. The course notes: "NeMo Guardrails ensures the ethical use of AI by monitoring and enforcing compliance with predefined rules, enhancing the safety and trustworthiness of LLM outputs." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA NeMo Framework User Guide.

NEW QUESTION # 71
When designing an experiment to compare the performance of two LLMs on a question-answering task, which statistical test is most appropriate to determine if the difference in their accuracy is significant, assuming the data follows a normal distribution?
  • A. Chi-squared test
  • B. Paired t-test
  • C. ANOVA test
  • D. Mann-Whitney U test
Answer: B
Explanation:
The paired t-test is the most appropriate statistical test to compare the performance (e.g., accuracy) of two large language models (LLMs) on the same question-answering dataset, assuming the data follows a normal distribution. This test evaluates whether the mean difference in paired observations (e.g., accuracy on each question) is statistically significant. NVIDIA's documentation on model evaluation in NeMo suggests using paired statistical tests for comparing model performance on identical datasets to account for correlated errors.
Option A (Chi-squared test) is for categorical data, not continuous metrics like accuracy. Option C (Mann- Whitney U test) is non-parametric and used for non-normal data. Option D (ANOVA) is for comparing more than two groups, not two models.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplear ... del_finetuning.html

NEW QUESTION # 72
What is the main difference between forward diffusion and reverse diffusion in diffusion models of Generative AI?
  • A. Forward diffusion uses feed-forward networks, while reverse diffusion uses recurrent networks.
  • B. Forward diffusion focuses on generating a sample from a given noise vector, while reverse diffusion reverses the process by estimating the latent space representation of a given sample.
  • C. Forward diffusion uses bottom-up processing, while reverse diffusion uses top-down processing to generate samples from noise vectors.
  • D. Forward diffusion focuses on progressively injecting noise into data, while reverse diffusion focuses on generating new samples from the given noise vectors.
Answer: D
Explanation:
Diffusion models, a class of generative AI models, operate in two phases: forward diffusion and reverse diffusion. According to NVIDIA's documentation on generative AI (e.g., in the context of NVIDIA's work on generative models), forward diffusion progressively injects noise into a data sample (e.g., an image or text embedding) over multiple steps, transforming it into a noise distribution. Reverse diffusion, conversely, starts with a noise vector and iteratively denoises it to generate a new sample that resembles the training data distribution. This process is central tomodels like DDPM (Denoising Diffusion Probabilistic Models). Option A is incorrect, as forward diffusion adds noise, not generates samples. Option B is false, as diffusion models typically use convolutional or transformer-based architectures, not recurrent networks. Option C is misleading, as diffusion does not align with bottom-up/top-down processing paradigms.
References:
NVIDIA Generative AI Documentation: https://www.nvidia.com/en-us/ai-data-science/generative-ai/ Ho, J., et al. (2020). "Denoising Diffusion Probabilistic Models."

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