FANTASTIC EXAM DUMPS NCA-GENM ZIP, NEW NCA-GENM EXAM QUESTIONS

Fantastic Exam Dumps NCA-GENM Zip, New NCA-GENM Exam Questions

Fantastic Exam Dumps NCA-GENM Zip, New NCA-GENM Exam Questions

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New NCA-GENM Exam Questions, NCA-GENM Actual Test Answers

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NVIDIA Generative AI Multimodal Sample Questions (Q248-Q253):

NEW QUESTION # 248
You're building a system to translate customer service chat logs into summaries that a human agent can quickly review The chat logs are often informal, contain slang, and have grammatical errors. Which prompt engineering technique is MOST likely to improve the quality and accuracy of the summaries generated by a large language model (LLM)?

  • A. Using a negative constraint prompt, explicitly stating what the LLM should not include in the summary (e.g., 'Do not include greetings or farewells.').
  • B. Using chain-of-thought prompting to encourage the LLM to explain its reasoning process before generating the summary.
  • C. Using a few-shot prompt with several examples of chat logs and their ideal summaries, explicitly demonstrating how to handle informality and errors.
  • D. Using a zero-shot prompt with a simple instruction like 'Summarize this chat log.'
  • E. Using a template prompt with predefined sections and keywords to guide the summarization process and ensure consistency across different chat logs.

Answer: A,B,C,E

Explanation:
Few-shot prompting provides the LLM with examples to learn from, allowing it to better handle the nuances of informal language and errors. Chain-of-thought helps the model reason step-by-step, leading to better summaries. Negative constraints prevent irrelevant information. Template prompts provide structure and consistency. A zero-shot prompt is less effective in this scenario due to the complexity of the input data.


NEW QUESTION # 249
You are building a system that uses a Generative A1 model that combines images and natural language prompts to create photorealistic images. The training process is computationally intensive. Which NVIDIA technology is best suited to accelerate the training of this Generative A1 model, especially if it is distributed across multiple GPUs?

  • A. NVIDIA optiX
  • B. NVIDIA TensorRT
  • C. NVIDIA NeMo
  • D. NVIDIA DALI
  • E. NVIDIA NCCL

Answer: E

Explanation:
NVIDIA NCCL (NVIDIA Collective Communications Library) is designed for efficient multi-GPU and multi-node communication. It enables faster distributed training of deep learning models by optimizing data transfers and synchronization between GPUs, thereby accelerating the entire training process. While OptiX is for ray tracing, TensorRT is for inference optimization, NeMo is for conversational A1 model development, and DALI is for data loading and preprocessing, NCCL is specifically designed for accelerating distributed training.


NEW QUESTION # 250
You are building a system that uses audio and video to detect emotional states of a user. What are the challenges to this system?

  • A. All of the above.
  • B. Variations in background noise affecting audio quality.
  • C. Synchronization issues between audio and video streams.
  • D. Differences in lighting conditions influencing facial expression recognition.
  • E. Subjectivity in emotional expression across cultures and individuals.

Answer: A

Explanation:
All the options pose challenges. Background noise affects audio analysis, varying lighting impairs facial recognition, subjective emotional expression necessitates robust models, and synchronization issues hinder accurate multimodal integration.


NEW QUESTION # 251
You are experimenting with different loss functions for training a Variational Autoencoder (VAE) to generate images. You observe that using only the reconstruction loss (e.g., Mean Squared Error) results in blurry images. What other loss component is typically added to the VAE objective function to encourage the latent space to be well-structured and generate sharper images?

  • A. Kullback-Leibler (KL) divergence loss
  • B. Cross-entropy loss
  • C. Hinge loss
  • D. Contrastive loss
  • E. Perceptual loss

Answer: A

Explanation:
The Kullback-Leibler (KL) divergence loss is a crucial component of the VAE objective function. It measures the difference between the learned latent space distribution and a prior distribution (typically a standard Gaussian). Adding the KL divergence loss encourages the latent space to be well-structured and continuous, which helps generate sharper and more realistic images. The other loss functions serve different purposes and are not typically used in VAEs for this specific reason. Cross-entropy is for classification. Perceptual loss helps in transferring styles. Contrastive loss used to learn embedding. Hinge loss mostly used in SVM.


NEW QUESTION # 252
You are developing a system to summarize patient medical records, which include doctor's notes (text), lab results (time-series data), and X-ray images. Which of the following techniques would be MOST effective in integrating these diverse data types to generate a coherent and comprehensive summary?

  • A. Use a multimodal transformer model that can process text, time-series, and image data as input, creating a joint representation for summarization.
  • B. Ignore the lab results and X-ray images and focus only on summarizing the doctor's notes.
  • C. Train separate summarization models for each data type and concatenate the resulting summaries.
  • D. Convert all data types to text using OCR and other techniques, then train a single text summarization model.
  • E. Use separate summarization models, weigh them by their perceived information content and average.

Answer: A

Explanation:
A multimodal transformer model is designed to handle different data types as input and learn relationships between them, generating a coherent and comprehensive summary. Other options may lead to loss of information or a disjointed summary.


NEW QUESTION # 253
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