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NVIDIA NCA-GENL Questions - Latest NCA-GENL Dumps [2025]

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NVIDIA NCA-GENL Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

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 5

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 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 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 8

Experiment Design

Topic 9

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 10

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.

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NVIDIA Generative AI LLMs Sample Questions (Q86-Q91):

NEW QUESTION # 86
Why might stemming or lemmatizing text be considered a beneficial preprocessing step in the context of computing TF-IDF vectors for a corpus?

A. It guarantees an increase in the accuracy of TF-IDF vectors by ensuring more precise word usage distinction.
B. It enhances the aesthetic appeal of the text, making it easier for readers to understand the document's content.
C. It reduces the number of unique tokens by collapsing variant forms of a word into their root form, potentially decreasing noise in the data.
D. It increases the complexity of the dataset by introducing more unique tokens, enhancing the distinctiveness of each document.

Answer: C

Explanation:
Stemming and lemmatizing are preprocessing techniques in NLP that reduce words to their root or base form, as discussed in NVIDIA's Generative AI and LLMs course. In the context of computing TF-IDF (Term Frequency-Inverse Document Frequency) vectors, these techniques are beneficial because they collapse variant forms of a word (e.g., "running," "ran" to "run") into a single token, reducing the number of unique tokens in the corpus. This decreases noise and dimensionality, improving the efficiency and effectiveness of TF-IDF representations for tasks like document classification or clustering. Option B is incorrect, as stemming and lemmatizing are not about aesthetics but about data preprocessing. Option C is wrong, as these techniques reduce, not increase, the number of unique tokens. Option D is inaccurate, as they do not guarantee accuracy improvements but rather reduce noise. The course states: "Stemming and lemmatizing reduce the number of unique tokens in a corpus by normalizing word forms, improving the quality of TF-IDF vectors by minimizing noise and dimensionality." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

NEW QUESTION # 87
What is the purpose of the NVIDIA NeMo Toolkit?

A. NeMo helps researchers to develop models that trade-off size with minimum loss impact.
B. NeMo helps researchers develop state-of-the-art models for computer vision based on convolutions.
C. NeMo facilitates the creation of models for speech recognition and natural language understanding.
D. NeMo focuses on the morphology of a language by studying its words, and how they are formed.

Answer: C

Explanation:
The NVIDIA NeMo Toolkit is a scalable, open-source framework designed to facilitate the development of state-of-the-art conversational AI models, particularly for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS). As highlighted in NVIDIA's Generative AI and LLMs course, NeMo provides modular, pre-built components and pre-trained models that researchers and developers can customize and fine-tune for tasks like speech recognition and natural language understanding.
It supports multi-GPU and multi-node training, leveraging PyTorch for efficient model development. Option A is incorrect, as NeMo does not focus on language morphology but on building AI models. Option B is wrong, as NeMo's primary goal is not model size trade-offs but comprehensive conversational AI development. Option D is inaccurate, as NeMo primarily targets speech and language tasks, not computer vision. The course notes: "NVIDIA NeMo is a toolkit for building conversational AI models, including Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models, enabling researchers to create and deploy advanced AI solutions." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA NeMo Framework User Guide.

NEW QUESTION # 88
When deploying an LLM using NVIDIA Triton Inference Server for a real-time chatbot application, which optimization technique is most effective for reducing latency while maintaining high throughput?

A. Reducing the input sequence length to minimize token processing.
B. Increasing the model's parameter count to improve response quality.
C. Enabling dynamic batching to process multiple requests simultaneously.
D. Switching to a CPU-based inference engine for better scalability.

Answer: C

Explanation:
NVIDIA Triton Inference Server is designed for high-performance model deployment, and dynamicbatching is a key optimization technique for reducing latency while maintaining high throughput in real-time applications like chatbots. Dynamic batching groups multiple inference requests into a single batch, leveraging GPU parallelism to process them simultaneously, thus reducing per-request latency. According to NVIDIA's Triton documentation, this is particularly effective for LLMs with variable input sizes, as it maximizes resource utilization. Option A is incorrect, as increasing parameters increases latency. Option C may reduce latency but sacrifices context and quality. Option D is false, as CPU-based inference is slower than GPU-based for LLMs.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server
/user-guide/docs/index.html

NEW QUESTION # 89
Which of the following best describes Word2vec?

A. A programming language used to build artificial intelligence models.
B. A deep learning algorithm used to generate word embeddings from text data.
C. A database management system designed for storing and querying word data.
D. A statistical technique used to analyze word frequency in a text corpus.

Answer: B

Explanation:
Word2Vec is a groundbreaking deep learning algorithm developed to create dense vector representations, or embeddings, of words based on their contextual usage in large text corpora. Unlike traditional methods like bag-of-words or TF-IDF, which rely on frequency counts and often result in sparse vectors, Word2Vec employs neural networks to learn continuous vector spaces where semantically similar words are positioned closer together. This enables machines to capture nuances such as synonyms, analogies, and relationships (e.
g., "king" - "man" + "woman" # "queen"). The algorithm operates through two primary architectures:
Continuous Bag-of-Words (CBOW), which predicts a target word from its surrounding context, and Skip- Gram, which does the reverse by predicting context words from a target word. Skip-Gram is particularly effective for rare words and larger datasets, while CBOW is faster and better for frequent words. In the context of NVIDIA's Generative AI and LLMs course, Word2Vec is highlighted as a foundational step in the evolution of text embeddings in natural language processing (NLP) tasks, paving the way for more advanced models like RNN-based embeddings and Transformers. This is essential for understanding how LLMs build upon these embeddings for tasks such as semantic analysis and language generation. Exact extract from the course description: "Understand how text embeddings have rapidly evolved in NLP tasks such as Word2Vec, recurrent neural network (RNN)-based embeddings, and Transformers." This positions Word2Vec as a key deep learning technique for generating meaningful word vectors from text data, distinguishing it from mere statistical frequency analysis or unrelated tools like programming languages or databases

NEW QUESTION # 90
Which of the following claims is correct about quantization in the context of Deep Learning? (Pick the 2 correct responses)

A. Helps reduce memory requirements and achieve better cache utilization.
B. Quantization might help in saving power and reducing heat production.
C. It consists of removing a quantity of weights whose values are zero.
D. It only involves reducing the number of bits of the parameters.
E. It leads to a substantial loss of model accuracy.

Answer: A,B

Explanation:
Quantization in deep learning involves reducing the precision of model weights and activations (e.g., from 32- bit floating-point to 8-bit integers) to optimize performance. According to NVIDIA's documentation on model optimization and deployment (e.g., TensorRT and Triton Inference Server), quantization offers several benefits:
* Option A: Quantization reduces power consumption and heat production by lowering the computational intensity of operations, making it ideal for edge devices.
References:
NVIDIA TensorRT Documentation: https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html

NEW QUESTION # 91
......

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