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Free PDF NVIDIA - Trustable Valid Test NCA-GENM Test

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NVIDIA Generative AI Multimodal Sample Questions (Q31-Q36):

NEW QUESTION # 31
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. Reconstruction loss (MSE) + KL divergence loss + Cross-entropy loss between the generated recipe and a plausible recipe given the generated image embedding-
B. Reconstruction loss (MSE) + Perceptual loss (based on a pre-trained image classifier) only
C. KL divergence loss + Cosine Similarity loss between the generated image embedding and a text embedding of a random recipe.
D. Reconstruction loss (MSE) between the input image and the decoded image only
E. KL divergence loss only-

Answer: A

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 # 32
Which of the following NVIDIA tools or SDKs can MOST effectively be utilized to profile and optimize the performance of a computationally intensive multimodal generative A1 model running on NVIDIA GPUs? (Select TWO)

A. TensorRT for high-performance inference.
B. NVIDIA Omniverse for collaborative 3D workflows.
C. NVIDIA Nsight Systems for system-wide performance analysis.
D. CIJDA Toolkit for memory management.
E. NVIDIA Merlin for recommender systems.

Answer: A,C

Explanation:
Nsight Systems provides detailed system-wide performance analysis, allowing you to identify bottlenecks and optimize resource utilization. TensorRT is a high-performance inference SDK that can significantly speed up model execution. While the CUDA Toolkit is essential for GPU programming, Nsight Systems is better for profiling. Omniverse and Merlin are relevant to other domains, not general performance optimization. Memory management is important, but Nsight provides profiling for this, better answering the question.

NEW QUESTION # 33
You are building a multimodal model for medical diagnosis that combines patient medical history (text), medical images (X-rays, MRIs), and sensor data (heart rate, blood pressure). The dataset contains significant amounts of missing data across all modalities. What strategy is most appropriate for handling the missing data and ensuring the model's robustness and accuracy?

A. Training seperate models for each avalible modality.
B. Removing all patients with missing data to create a clean dataset.
C. Using a multimodal variational autoencoder (MVAE) to learn a joint latent representation of the data and impute missing values based on the observed modalities.
D. Using a Generative Adversarial Network(GAN) to impute missing values based on the other avalible modalities.
E. Imputing missing values using simple methods like mean imputation or filling with a constant value.

Answer: C,D

Explanation:
Removing patients with missing data can lead to a significant loss of information and bias the model. Simple imputation methods can introduce inaccuracies and fail to capture the relationships between modalities. Multimodal variational autoencoders (MVAEs) are specifically designed to handle missing data in multimodal datasets by learning a joint latent representation and imputing values based on the observed modalities. This approach is more robust and accurate than simple imputation methods. GAN can also be used to impute missing values.

NEW QUESTION # 34
You are working with a multimodal generative model that combines text and image inputs. The model's performance is suboptimal when generating images conditioned on complex text descriptions. Which data analysis technique would be MOST effective in identifying the root cause of this issue?

A. Measuring the frequency of different objects appearing in the images.
B. Calculating the mean pixel intensity of the images.
C. Calculating the average image resolution in the dataset.
D. Performing a sentiment analysis of the text descriptions to identify potential biases.
E. Analyzing the correlation between the complexity of the text descriptions (e.g., number of words, sentence structure) and the quality of the generated images.

Answer: E

Explanation:
Analyzing the correlation between text complexity and image quality directly investigates whether the model struggles with more intricate text descriptions. Sentiment analysis is relevant for identifying biases, but doesn't address the complexity issue directly. The average image resolution and pixel intensity are general image statistics and not specific to the multimodal problem. Object frequency in images can be useful but less direct than correlating text complexity with image quality.

NEW QUESTION # 35
You're building a multimodal model to predict stock prices using news articles (text) and historical price data (time series). You observe that the model performs well on the training data but poorly on unseen dat a. Which of the following techniques would be MOST effective in addressing this issue?

A. Increase the size of the training dataset by collecting more news articles and historical price data.
B. Implement early stopping based on the performance of the model on a validation set.
C. All of the above
D. Add L1 or L2 regularization to the model's parameters.
E. Use data augmentation techniques to generate more diverse variations of the existing data.

Answer: B

Explanation:
All the listed techniques combat overfitting. Increasing data size provides more examples. Regularization penalizes complex models. Early stopping prevents training too long on the training data. Data augmentation creates more robust and generalizable model by artifically expanding data through techniques like Tme series perturbation and Text paraphrasing.

NEW QUESTION # 36
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