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[General] Pass Guaranteed Quiz 2026 NVIDIA NCA-GENL Authoritative Valid Test Topics

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【General】 Pass Guaranteed Quiz 2026 NVIDIA NCA-GENL Authoritative Valid Test Topics

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NVIDIA NCA-GENL Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • Experiment Design
Topic 3
  • 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 4
  • 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 5
  • 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 6
  • 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 7
  • 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 8
  • 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 9
  • 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 10
  • 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.

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NVIDIA Generative AI LLMs Sample Questions (Q87-Q92):NEW QUESTION # 87
You are working on developing an application to classify images of animals and need to train a neural model.
However, you have a limited amount of labeled data. Which technique can you use to leverage the knowledge from a model pre-trained on a different task to improve the performance of your new model?
  • A. Random initialization
  • B. Dropout
  • C. Early stopping
  • D. Transfer learning
Answer: D
Explanation:
Transfer learning is a technique where a model pre-trained on a large, general dataset (e.g., ImageNet for computer vision) is fine-tuned for a specific task with limited data. NVIDIA's Deep Learning AI documentation, particularly for frameworks like NeMo and TensorRT, emphasizes transfer learning as a powerful approach to improve model performance when labeled data is scarce. For example, a pre-trained convolutional neural network (CNN) can be fine-tuned for animal image classification by reusing its learned features (e.g., edge detection) and adapting the final layers to the new task. Option A (dropout) is a regularization technique, not a knowledge transfer method. Option B (random initialization) discards pre- trained knowledge. Option D (early stopping) prevents overfitting but does not leverage pre-trained models.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplear ... /docs/en/stable/nlp
/model_finetuning.html
NVIDIA Deep Learning AI:https://www.nvidia.com/en-us/deep-learning-ai/

NEW QUESTION # 88
What is the fundamental role of LangChain in an LLM workflow?
  • A. To directly manage the hardware resources used by LLMs.
  • B. To orchestrate LLM components into complex workflows.
  • C. To act as a replacement for traditional programming languages.
  • D. To reduce the size of AI foundation models.
Answer: B
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 # 89
Which tool would you use to select training data with specific keywords?
  • A. ActionScript
  • B. JSON parser
  • C. Tableau dashboard
  • D. Regular expression filter
Answer: D
Explanation:
Regular expression (regex) filters are widely used in data preprocessing to select text data containing specific keywords or patterns. NVIDIA's documentation on data preprocessing for NLP tasks, such as in NeMo, highlights regex as a standard tool for filtering datasets based on textual criteria, enabling efficient data curation. For example, a regex pattern like .*keyword.* can select all texts containing "keyword." Option A (ActionScript) is a programming language for multimedia, not data filtering. Option B (Tableau) is for visualization, not text filtering. Option C (JSON parser) is for structured data, not keyword-based text selection.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplear ... /docs/en/stable/nlp
/intro.html

NEW QUESTION # 90
Which Python library is specifically designed for working with large language models (LLMs)?
  • A. Pandas
  • B. Scikit-learn
  • C. NumPy
  • D. HuggingFace Transformers
Answer: D
Explanation:
The HuggingFace Transformers library is specifically designed for working with large language models (LLMs), providing tools for model training, fine-tuning, and inference with transformer-based architectures (e.
g., BERT, GPT, T5). NVIDIA's NeMo documentation often references HuggingFace Transformers for NLP tasks, as it supports integration with NVIDIA GPUs and frameworks like PyTorch for optimized performance.
Option A (NumPy) is for numerical computations, not LLMs. Option B (Pandas) is for data manipulation, not model-specific tasks. Option D (Scikit-learn) is for traditional machine learning, not transformer-based LLMs.
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 # 91
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. Chain-of-thought prompting with step-by-step reasoning examples.
  • B. Few-shot prompting with randomly selected examples.
  • C. Zero-shot prompting with a generic task description.
  • D. Retrieval-augmented generation with external mathematical databases.
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 # 92
......
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