Authorized Latest NCA-GENL Questions & Leader in Qualification Exams & High-quality NCA-GENL: NVIDIA Generative AI LLMs
Many don't find real NCA-GENL exam questions and face loss of money and time. Itcertking made an absolute gem of study material which carries actual NVIDIA NCA-GENL Exam Questions for the students so that they don't get confused in order to prepare for NVIDIA NCA-GENL Exam and pass it with a good score. The NVIDIA NCA-GENL practice test questions are made by examination after consulting with a lot of professionals and receiving positive feedback from them.
NVIDIA NCA-GENL Exam Syllabus Topics:
Topic
Details
Topic 1
Topic 2
Topic 3
Topic 4
Topic 5
>> Latest NCA-GENL Questions <<
Valid NCA-GENL Study Plan - Detail NCA-GENL Explanation
We have strong technical and research capabilities on this career for the reason that we have a professional and specialized expert team devoting themselves on the compiling the latest and most precise NCA-GENL exam materials. All questions and answers of NCA-GENL learning guide are tested by professionals who have passed the NCA-GENL Exam. All the experts we hired have been engaged in professional qualification exams for many years. The hit rate for NCA-GENL exam torrent is as high as 99%. You will pass the NCA-GENL exam for sure with our NCA-GENL exam questions.
NVIDIA Generative AI LLMs Sample Questions (Q16-Q21):
NEW QUESTION # 16
Which of the following prompt engineering techniques is most effective for improving an LLM's performance on multi-step reasoning tasks?
Answer: B
Explanation:
Chain-of-thought (CoT) prompting is a highly effective technique for improving large language model (LLM) performance on multi-step reasoning tasks. By including explicit intermediate steps in the prompt, CoT guides the model to break down complex problems into manageable parts, improving reasoning accuracy. NVIDIA's NeMo documentation on prompt engineering highlights CoT as a powerful method for tasks like mathematical reasoning or logical problem-solving, as it leverages the model's ability to follow structured reasoning paths. Option A is incorrect, as retrieval-augmented generation (RAG) without context is less effective for reasoning tasks. Option B is wrong, as unrelated examples in few-shot prompting do not aid reasoning. Option C (zero-shot prompting) is less effective than CoT for complex reasoning.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html Wei, J., et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models."
NEW QUESTION # 17
When fine-tuning an LLM for a specific application, why is it essential to perform exploratory data analysis (EDA) on the new training dataset?
Answer: D
Explanation:
Exploratory Data Analysis (EDA) is a critical step in fine-tuning large language models (LLMs) to understand the characteristics of the new training dataset. NVIDIA's NeMo documentation on data preprocessing for NLP tasks emphasizes that EDA helps uncover patterns (e.g., class distributions, word frequencies) and anomalies (e.g., outliers, missing values) that can affect model performance. For example, EDA might reveal imbalanced classes or noisy data, prompting preprocessing steps like data cleaning or augmentation. Option B is incorrect, as learning rate selection is part of model training, not EDA. Option C is unrelated, as EDA does not assess computational resources. Option D is false, as the number of layers is a model architecture decision, not derived from EDA.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
NEW QUESTION # 18
Why do we need positional encoding in transformer-based models?
Answer: A
Explanation:
Positional encoding is a critical component in transformer-based models because, unlike recurrent neural networks (RNNs), transformers process input sequences in parallel and lack an inherent sense of word order.
Positional encoding addresses this by embedding information about the position of each token in the sequence, enabling the model to understand the sequential relationships between tokens. According to the original transformer paper ("Attention is All You Need" by Vaswani et al., 2017), positional encodings are added to the input embeddings to provide the model with information about the relative or absolute position of tokens. NVIDIA's documentation on transformer-based models, such as those supported by the NeMo framework, emphasizes that positional encodings are typically implemented using sinusoidal functions or learned embeddings to preserve sequence order, which is essential for tasks like natural language processing (NLP). Options B, C, and D are incorrect because positional encoding does not address overfitting, dimensionality reduction, or throughput directly; these are handled by other techniques like regularization, dimensionality reduction methods, or hardware optimization.
References:
Vaswani, A., et al. (2017). "Attention is All You Need."
NVIDIA NeMo Documentation:https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/intro.html
NEW QUESTION # 19
How does A/B testing contribute to the optimization of deep learning models' performance and effectiveness in real-world applications? (Pick the 2 correct responses)
Answer: A,B
Explanation:
A/B testing is a controlled experimentation technique used to compare two versions of a system to determine which performs better. In the context of deep learning, NVIDIA's documentation on model optimization and deployment (e.g., Triton Inference Server) highlights its use in evaluating model performance:
* Option A: A/B testing validates changes (e.g., model updates or new features) by statistically comparing outcomes (e.g., accuracy or user engagement), enabling data-driven optimization decisions.
References:
NVIDIA Triton Inference Server Documentation: https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html
NEW QUESTION # 20
What is the main difference between forward diffusion and reverse diffusion in diffusion models of Generative AI?
Answer: C
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."
NEW QUESTION # 21
......
Many people are afraid of walking out of their comfortable zones. So it is difficult for them to try new things. But you will never grow up if you reject new attempt. Now, our NCA-GENL study quiz can help you have a positive change. It is important for you to keep a positive mind. Our NCA-GENL Practice Guide can become your new attempt. And our NCA-GENL exam braindumps will bring out the most effective rewards to you as long as you study with them.
Valid NCA-GENL Study Plan: https://www.itcertking.com/NCA-GENL_exam.html