100% PASS TRUSTABLE NVIDIA - VALID NCA-GENM TEST VCE

100% Pass Trustable NVIDIA - Valid NCA-GENM Test Vce

100% Pass Trustable NVIDIA - Valid NCA-GENM Test Vce

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To stay updated and competitive in the market you have to upgrade your skills and knowledge level. Fortunately, with the NVIDIA Generative AI Multimodal (NCA-GENM) certification exam you can do this job easily and quickly. To do this you just need to pass the NVIDIA Generative AI Multimodal (NCA-GENM) certification exam. The NVIDIA Generative AI Multimodal (NCA-GENM) certification exam is the top-rated and career advancement NVIDIA NCA-GENM certification in the market.

NVIDIA Generative AI Multimodal Sample Questions (Q315-Q320):

NEW QUESTION # 315
You're building a system that takes a medical image (e.g., X-ray) and a patient's medical history (text) as input, predicting the likelihood of a specific disease. You want to use SHAP (SHapley Additive exPlanations) values to explain the model's predictions. How would you adapt SHAP to handle both image and text inputs effectively?

  • A. Use a multimodal SHAP implementation that is designed to handle both image and text features simultaneously, considering their interaction.
  • B. Treat the image and text as separate models and explain each independently.
  • C. Use DeepExplainer for the image component and a simple linear SHAP explainer for the text.
  • D. Apply KernelSHAP separately to the image and text, then combine the results.
  • E. Represent both the image and text as numerical vectors and then apply a standard SHAP explainer.

Answer: A

Explanation:
The best approach is to use a multimodal SHAP implementation that considers the interaction between image and text features. This ensures a holistic explanation of the model's prediction based on both modalities. Treating them separately or simply concatenating features ignores potential synergistic effects.


NEW QUESTION # 316
You are developing a virtual assistant using NVIDIAACE. You want to ensure that the avatar's facial expressions and lip movements are synchronized with the generated speech in real-time. Which NVIDIA SDKs and ACE components are essential for achieving this?

  • A. Triton Inference Server for deploying all AI models, Riva for voice cloning, and Omniverse for character creation.
  • B. NeMo for text-to-speech, Audio2Face for generating blendshape weights, and a real-time rendering engine (e.g., Unity or Unreal Engine) to drive the avatar.
  • C. CUDA for GPU acceleration, TensorRT for model optimization, and Audi02Emotion for expression generation.
  • D. Riva for speech synthesis, NeMo for language modeling, and Audi02Face for animation.
  • E. Riva for speech recognition, Triton Inference Server for model deployment, and Omniverse for 3D rendering.

Answer: B

Explanation:
Achieving real-time synchronized facial animation requires a text-to-speech engine (NeMo), a system to generate blendshape weights from the audio (Audi02Face), and a rendering engine to display the animated avatar. Riva provides speech recognition, not necessarily synthesis in this case. While Omniverse is useful for 3D rendering (B,D), it isn't strictly required. CUDA and TensorRT (E) are foundational but don't directly address animation.


NEW QUESTION # 317
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. Cross-entropy loss
  • B. Perceptual loss
  • C. Kullback-Leibler (KL) divergence loss
  • D. Contrastive loss
  • E. Hinge loss

Answer: C

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 # 318
You are evaluating two different generative A1 model architectures (Model A and Model B) for image generation. You use the Frechet Inception Distance (FID) score as your primary evaluation metric. Model A has a lower FID score than Model B. Which of the following statements are MOST accurate regarding the interpretation of the FID scores? (Select TWO)

  • A. Model B necessarily has better performance on downstream tasks using the generated images.
  • B. Model A generates images that have a distribution more similar to the real image distribution used for calculating the FID score.
  • C. Model B generates images that are more diverse than Model A.
  • D. Model A generates images that are more visually appealing to human observers.
  • E. Model A is less likely to suffer from mode collapse than Model B.

Answer: B,E

Explanation:
A lower FID score indicates that the generated images are statistically more similar to the real images (B). It also suggests that Model A is less prone to mode collapse (D), as it captures the data distribution better. FID score doesn't guarantee visual appeal (A) or better performance on downstream tasks (E). Diversity (C) isn't directly implied by a lower FID score alone.


NEW QUESTION # 319
You're tasked with building a model that can generate recipes from images of food. You decide to use a Variational Autoencoder (VAE) architecture. What would be a suitable loss function combination for this task, considering both reconstruction accuracy and recipe relevance?

  • A. KL divergence loss only-
  • B. Reconstruction loss (MSE) + KL divergence loss + Cross-entropy loss between the generated recipe and a plausible recipe given the generated image embedding-
  • C. Reconstruction loss (MSE) + Perceptual loss (based on a pre-trained image classifier) only
  • D. KL divergence loss + Cosine Similarity loss between the generated image embedding and a text embedding of a random recipe.
  • E. Reconstruction loss (MSE) between the input image and the decoded image only

Answer: B

Explanation:
The Reconstruction loss ensures the generated image is similar to the input. KL divergence enforces a smooth latent space. The Cross-entropy loss ensures the generated recipe is relevant to the decoded image. Perceptual loss, while helpful for image quality, doesn't directly address recipe relevance. Using a text embedding of a random recipe would not guide the model towards generating relevant recipes.


NEW QUESTION # 320
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