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[General] 2026 High-quality NCA-GENL Test Centres | NCA-GENL 100% Free Certification Exam

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【General】 2026 High-quality NCA-GENL Test Centres | NCA-GENL 100% Free Certification Exam

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NVIDIA NCA-GENL Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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 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
  • Experiment Design
Topic 6
  • 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 7
  • 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 8
  • 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.
Topic 9
  • 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 10
  • 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 (Q28-Q33):NEW QUESTION # 28
Which of the following claims is correct about TensorRT and ONNX?
  • A. TensorRT is used for model creation and ONNX is used for model deployment.
  • B. TensorRT is used for model deployment and ONNX is used for model creation.
  • C. TensorRT is used for model creation and ONNX is used for model interchange.
  • D. TensorRT is used for model deployment and ONNX is used for model interchange.
Answer: D
Explanation:
NVIDIA TensorRT is a deep learning inference library used to optimize and deploy models for high- performance inference, while ONNX (Open Neural Network Exchange) is a format for model interchange, enabling models to be shared across different frameworks, as covered in NVIDIA's Generative AI and LLMs course. TensorRT optimizes models (e.g., via layer fusion and quantization) for deployment on NVIDIA GPUs, while ONNX ensures portability by providing a standardized model representation. Option B is incorrect, as ONNX is not used for model creation but for interchange. Option C is wrong, as TensorRT is not for model creation but optimization and deployment. Option D is inaccurate, as ONNX is not for deployment but for model sharing. The course notes: "TensorRT optimizes and deploys deep learning models for inference, while ONNX enables model interchange across frameworks for portability." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 29
What do we usually refer to as generative AI?
  • A. A branch of artificial intelligence that focuses on creating models that can generate new and original data.
  • B. A branch of artificial intelligence that focuses on analyzing and interpreting existing data.
  • C. A branch of artificial intelligence that focuses on auto generation of models for classification.
  • D. A branch of artificial intelligence that focuses on improving the efficiency of existing models.
Answer: A
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 # 30
Which technique is used in prompt engineering to guide LLMs in generating more accurate and contextually appropriate responses?
  • A. Choosing another model architecture.
  • B. Increasing the model's parameter count.
  • C. Training the model with additional data.
  • D. Leveraging the system message.
Answer: D
Explanation:
Prompt engineering involves designing inputs to guide large language models (LLMs) to produce desired outputs without modifying the model itself. Leveraging the system message is a key technique, where a predefined instruction or context is provided to the LLM to set the tone, role, or constraints for its responses.
NVIDIA's NeMo framework documentation on conversational AI highlights the use of system messages to improve the contextual accuracy of LLMs, especially in dialogue systems or task-specific applications. For instance, a system message like "You are a helpful technical assistant" ensures responses align with the intended role. Options A, B, and C involve model training or architectural changes, which are not part of prompt engineering.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplear ... /docs/en/stable/nlp
/intro.html

NEW QUESTION # 31
Which of the following is a key characteristic of Rapid Application Development (RAD)?
  • A. Linear progression through predefined project phases.
  • B. Iterative prototyping with active user involvement.
  • C. Minimal user feedback during the development process.
  • D. Extensive upfront planning before any development.
Answer: B
Explanation:
Rapid Application Development (RAD) is a software development methodology that emphasizes iterative prototyping and active user involvement to accelerate development and ensure alignment with user needs.
NVIDIA's documentation on AI application development, particularly in the context of NGC (NVIDIA GPU Cloud) and software workflows, aligns with RAD principles for quickly building and iterating on AI-driven applications. RAD involves creating prototypes, gathering user feedback, and refining the application iteratively, unlike traditional waterfall models. Option B is incorrect, as RAD minimizes upfront planning in favor of flexibility. Option C describes a linear waterfall approach, not RAD. Option D is false, as RAD relies heavily on user feedback.
References:
NVIDIA NGC Documentation: https://docs.nvidia.com/ngc/ngc-overview/index.html

NEW QUESTION # 32
What is 'chunking' in Retrieval-Augmented Generation (RAG)?
  • A. A method used in RAG to generate random text.
  • B. A concept in RAG that refers to the training of large language models.
  • C. Rewrite blocks of text to fill a context window.
  • D. A technique used in RAG to split text into meaningful segments.
Answer: D
Explanation:
Chunking in Retrieval-Augmented Generation (RAG) refers to the process of splitting large text documents into smaller, meaningful segments (or chunks) to facilitate efficient retrieval and processing by the LLM.
According to NVIDIA's documentation on RAG workflows (e.g., in NeMo and Triton), chunking ensures that retrieved text fits within the model's context window and is relevant to the query, improving the quality of generated responses. For example, a long document might be divided into paragraphs or sentences to allow the retrieval component to select only the most pertinent chunks. Option A is incorrect because chunking does not involve rewriting text. Option B is wrong, as chunking is not about generating random text. Option C is unrelated, as chunking is not a training process.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplear ... able/nlp/intro.html Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks."

NEW QUESTION # 33
......
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