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[General] Trustable NVIDIA Exam Actual Tests–Useful NCA-GENL Free Dumps

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【General】 Trustable NVIDIA Exam Actual Tests–Useful NCA-GENL Free Dumps

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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
  • 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
  • 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 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
  • 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 7
  • 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 (Q94-Q99):NEW QUESTION # 94
What are the main advantages of instructed large language models over traditional, small language models (<
300M parameters)? (Pick the 2 correct responses)
  • A. Cheaper computational costs during inference.
  • B. Trained without the need for labeled data.
  • C. Smaller latency, higher throughput.
  • D. Single generic model can do more than one task.
  • E. It is easier to explain the predictions.
Answer: A,D
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 ... able/nlp/intro.html Brown, T., et al. (2020). "Language Models are Few-Shot Learners."

NEW QUESTION # 95
When using NVIDIA RAPIDS to accelerate data preprocessing for an LLM fine-tuning pipeline, which specific feature of RAPIDS cuDF enables faster data manipulation compared to traditional CPU-based Pandas?
  • A. Integration with cloud-based storage for distributed data access.
  • B. Automatic parallelization of Python code across CPU cores.
  • C. Conversion of Pandas DataFrames to SQL tables for faster querying.
  • D. GPU-accelerated columnar data processing with zero-copy memory access.
Answer: D
Explanation:
NVIDIA RAPIDS cuDF is a GPU-accelerated library that mimics Pandas' API but performs data manipulation on GPUs, significantly speeding up preprocessing tasks for LLM fine-tuning. The key feature enabling this performance is GPU-accelerated columnar data processing with zero-copy memory access, which allows cuDF to leverage the parallel processing power of GPUs and avoid unnecessary data transfers between CPU and GPU memory. According to NVIDIA's RAPIDS documentation, cuDF's columnar format and CUDA-based operations enable orders-of-magnitude faster data operations (e.g., filtering, grouping) compared to CPU-based Pandas. Option A is incorrect, as cuDF uses GPUs, not CPUs. Option C is false, as cloud integration is not a core cuDF feature. Option D is wrong, as cuDF does not rely on SQL tables.
References:
NVIDIA RAPIDS Documentation: https://rapids.ai/

NEW QUESTION # 96
What is 'chunking' in Retrieval-Augmented Generation (RAG)?
  • A. Rewrite blocks of text to fill a context window.
  • B. A technique used in RAG to split text into meaningful segments.
  • C. A concept in RAG that refers to the training of large language models.
  • D. A method used in RAG to generate random text.
Answer: B
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 ... /docs/en/stable/nlp
/intro.html
Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks."

NEW QUESTION # 97
What is confidential computing?
  • A. A process for designing and applying AI systems in a manner that is explainable, fair, and verifiable.
  • B. A method for interpreting and integrating various forms of data in AI systems.
  • C. A technique for aligning the output of the AI models with human beliefs.
  • D. A technique for securing computer hardware and software from potential threats.
Answer: D
Explanation:
Confidential computing is a technique for securing computer hardware and software from potential threats by protecting data in use, as covered in NVIDIA's Generative AI and LLMs course. It ensures that sensitive data, such as model weights or user inputs, remains encrypted during processing, using technologies like secure enclaves or trusted execution environments (e.g., NVIDIA H100 GPUs with confidential computing capabilities). This enhances the security of AI systems. Option B is incorrect, as it describes Trustworthy AI principles, not confidential computing. Option C is wrong, as aligning outputs with human beliefs is unrelated to security. Option D is inaccurate, as data integration is not the focus of confidential computing. The course notes: "Confidential computing secures AI systems by protecting data in use, leveraging trusted execution environments to safeguard sensitive information during processing." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 98
Imagine you are training an LLM consisting of billions of parameters and your training dataset is significantly larger than the available RAM in your system. Which of the following would be an alternative?
  • A. Using the GPU memory to extend the RAM capacity for storing the dataset and move the dataset in and out of the GPU, using the PCI bandwidth possibly.
  • B. Discarding the excess of data and pruning the dataset to the capacity of the RAM, resulting in reduced latency during inference.
  • C. Using a memory-mapped file that allows the library to access and operate on elements of the dataset without needing to fully load it into memory.
  • D. Eliminating sentences that are syntactically different by semantically equivalent, possibly reducing the risk of the model hallucinating as it is trained to get to the point.
Answer: C
Explanation:
When training an LLM with a dataset larger than available RAM, using a memory-mapped file is an effective alternative, as discussed in NVIDIA's Generative AI and LLMs course. Memory-mapped files allow the system to access portions of the dataset directly from disk without loading the entire dataset into RAM, enabling efficient handling of large datasets. This approach leverages virtual memory to map file contents to memory, reducing memory bottlenecks. Option A is incorrect, as moving large datasets in and out of GPU memory via PCI bandwidth is inefficient and not a standard practice for dataset storage. Option C is wrong, as discarding data reduces model quality and is not a scalable solution. Option D is inaccurate, as eliminating semantically equivalent sentences is a specific preprocessing step that does not address memory constraints.
The course states: "Memory-mapped files enable efficient training of LLMs on large datasets by accessing data from disk without loading it fully into RAM, overcoming memory limitations." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 99
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