AUTHORIZED NCA-GENM CERTIFICATION | NCA-GENM RELATED CERTIFICATIONS

Authorized NCA-GENM Certification | NCA-GENM Related Certifications

Authorized NCA-GENM Certification | NCA-GENM Related Certifications

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Tags: Authorized NCA-GENM Certification, NCA-GENM Related Certifications, NCA-GENM Latest Exam Guide, NCA-GENM Valid Test Practice, Authentic NCA-GENM Exam Hub

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NVIDIA Generative AI Multimodal Sample Questions (Q331-Q336):

NEW QUESTION # 331
Consider the following code snippet which aims to create a custom prompt for a Stable Diffusion model using the 'diffusers* library. The goal is to generate an image of 'a cat wearing a hat sitting on a chair'. Which of the following modifications would MOST effectively improve the quality and coherence of the generated image?

  • A. Using a smaller image resolution to reduce computational cost.
  • B. Increasing the 'guidance_scale' and adding a negative prompt such as 'blurry, distorted'.
  • C. Adding more unrelated keywords to the prompt to increase diversity.
  • D. Decreasing the number of inference steps to speed up the generation process.
  • E. Removing the word 'sitting' from the prompt to make it more general.

Answer: B

Explanation:
Increasing the 'guidance_scales forces the generated image to adhere more closely to the prompt. Adding a negative prompt helps remove unwanted artifacts and improves image quality. Negative prompt is a well know prompt engginerring technique to remove unwanted objects or attributes.


NEW QUESTION # 332
Consider the following scenario: You're training a GAN for generating high-resolution images (e.g., 1024x1024). You notice that the training process is unstable, with the generator and discriminator constantly oscillating. Which of the following architectural modifications and training techniques could help stabilize the training process?

  • A. Applying batch normalization in both the generator and discriminator.
  • B. Using ReLU activation functions in the discriminator.
  • C. Increasing the learning rate of both the generator and discriminator.
  • D. Using Wasserstein GAN (WGAN) with gradient penalty (GP).
  • E. Replacing standard convolutional layers with transposed convolutional layers in the generator.

Answer: A,D

Explanation:
WGAN with gradient penalty (GP) addresses the instability caused by the Jensen-Shannon divergence used in standard GANs. Batch normalization can help stabilize training by reducing internal covariate shift. Transposed convolutions are a common practice but don't inherently stabilize training. Increasing the learning rate can exacerbate instability. ReLU activation can lead to vanishing gradients.


NEW QUESTION # 333
Which of the following is the MOST important factor in ensuring the 'trustworthiness' of a multimodal Generative AI model used for a safety-critical application (e.g., medical diagnosis)?

  • A. Low computational cost for inference.
  • B. Use of the latest deep learning architecture.
  • C. Ability to generate diverse outputs.
  • D. High accuracy on the training dataset.
  • E. Explainability and interpretability of the model's decisions.

Answer: E

Explanation:
For safety-critical applications, understanding why a model makes a certain decision is crucial. Explainability allows users to verify the model's reasoning and identify potential biases or errors. High accuracy alone is not sufficient if the model's decision-making process is opaque. While computational cost and architecture are important, they are secondary to trustworthiness in this context.


NEW QUESTION # 334
Which of the following evaluation metrics is MOST appropriate for assessing the performance of a multimodal generative A1 model that generates image captions based on images and audio descriptions?

  • A. Mean Squared Error (MSE)
  • B. Perplexity
  • C. BLEU (Bilingual Evaluation Understudy)
  • D. Root Mean Squared Error (RMSE)
  • E. Inception Score

Answer: C

Explanation:
BLEU (B) is specifically designed to evaluate the quality of generated text by comparing it to reference captions. Perplexity is more suitable for language models, Inception Score for generated images, and MSE/RMSE for regression tasks.


NEW QUESTION # 335
You're developing an Avatar Cloud Engine (ACE) application to create a real-time, interactive virtual assistant. The assistant needs to respond to user speech, understand their intent, and generate appropriate responses. Which sequence of NVIDIA SDKs would provide the MOST complete solution for this task?

  • A. NeMo (for training a custom language model) -> Triton Inference Server (for serving the trained language model) -> ACE (for avatar rendering and animation).
  • B. Riva (for speech recognition and synthesis) -> NeMo (for natural language understanding and response generation) -> Triton Inference Server (for model deployment) ACE (for avatar rendering and animation).
  • C. CUDA (For running deep learning workloads)-> Riva (for speech recognition and synthesis) -> ACE (for avatar rendering and animation).
  • D. Triton Inference Server (for serving all models) -> Riva (for speech recognition and synthesis) ACE (for avatar rendering and animation).
  • E. Riva (for speech recognition and synthesis) -> Triton Inference Server (for serving a pre-trained chatbot model) -> ACE (for avatar rendering and animation).

Answer: B

Explanation:
The most complete solution involves speech recognition and synthesis (Riva), natural language understanding and response generation (NeMo), efficient model deployment (Triton), and avatar rendering/animation (ACE). Option A is missing the NLU/response generation component. Option B doesn't directly address speech recognition. Option D puts Triton in the wrong order. Option E is too low level and doesn't leverage the higher-level SDKs effectively. The Correct sequence ensures the speech is converted to text, understands the intent, creates an appropriate response using a trained Model/architecture and finally shows the response through an Avatar.


NEW QUESTION # 336
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