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NVIDIA Generative AI Multimodal Sample Questions (Q362-Q367):

NEW QUESTION # 362
You are fine-tuning a large pre-trained language model for a specific downstream task. During training, you observe that the model performs well on the training data but generalizes poorly to the validation dat a. Which of the following strategies could help improve the model's generalization performance?

A. Decrease the learning rate.
B. Implement early stopping based on the validation loss.
C. Increase the weight decay (L2 regularization).
D. Increase the training data size by collecting more data.
E. Increase the learning rate.

Answer: A,B,C,D

Explanation:
Decreasing the learning rate can help the model to converge to a better solution and avoid overfitting. Increasing the training data size provides the model with more examples to learn from, improving generalization. Early stopping prevents the model from training for too long and overfitting the training data. Increasing weight decay adds more regularization, preventing the model from learning overly complex patterns. Increasing the learning rate might worsen overfitting.

NEW QUESTION # 363
You are tasked with deploying a generative A1 model for image inpainting using Triton Inference Server. The model takes an image with masked regions as input and outputs the completed image. You need to pre-process the input image before sending it to the server.
Which pre-processing steps are crucial for ensuring optimal performance and accuracy of the inpainting model?

A. Creating a binary mask indicating the regions to be inpainted.
B. Sharpening the image to enhance details.
C. Converting the image to grayscale.
D. Resizing the image to a fixed resolution and normalizing pixel values to a specific range (e.g., [0, 1] or [-1, 1]).
E. Applying data augmentation techniques like random rotations and flips.

Answer: A,D

Explanation:
Resizing and normalization ensure the input image conforms to the model's expected input size and data range. A binary mask is essential to indicate which regions the model should inpaint. Converting to grayscale would remove color information, hindering the inpainting process. Data augmentation should be done during the training phase. Sharpening is not typically a necessary pre-processing step for inpainting.

NEW QUESTION # 364
You've trained a large multimodal model that takes text and images as input and generates creative stories. While the model produces high-quality stories in general, it occasionally generates outputs that are factually incorrect or nonsensical. Which of the following techniques would be MOST effective in improving the model's factual accuracy and coherence?

A. Increasing the model size by adding more layers.
B. Implementing a retrieval-augmented generation (RAG) approach.
C. Reducing the temperature parameter during generation.
D. Removing dropout layers.
E. Training the model on a smaller dataset.

Answer: B

Explanation:
Retrieval-augmented generation (RAG) involves retrieving relevant information from an external knowledge source (e.g., a database or a collection of documents) and incorporating it into the model's generation process. This can help the model generate more factually accurate and coherent outputs by grounding its responses in external knowledge. Increasing the model size might improve performance, but doesn't guarantee factual accuracy. Reducing temperature makes the output more predictable but doesn't address factual correctness. Smaller datasets and removing dropout would likely worsen performance.

NEW QUESTION # 365
You are tasked with optimizing a Generative A1 model that processes both image and text dat a. The current model uses a simple concatenation of image features (extracted from a ResNet-50) and text embeddings (from BERT) as input to a transformer. You observe that the model struggles to generate coherent descriptions for complex images. Which of the following optimization strategies would be MOST effective in improving the model's understanding of the multimodal input?

A. Switch to a larger ResNet architecture (e.g., ResNet-101 ) while keeping the concatenation.
B. Increase the size of the transformer encoder layers.
C. Augment the text data with more examples.
D. Replace concatenation with a cross-attention mechanism between image features and text embeddings.
E. Reduce the learning rate by a factor of 10.

Answer: D

Explanation:
Cross-attention allows the model to learn which parts of the image are most relevant to each word in the text, enabling a more nuanced understanding of the relationship between the two modalities. Concatenation treats all features equally, which is less effective. Increasing transformer size or ResNet architecture might help but doesn't address the core issue of multimodal interaction.

NEW QUESTION # 366
You are building a system that generates image captions from images and vice vers a. Which evaluation metric(s) are MOST appropriate to assess the quality of the generated content? (Select all that apply)

A. Accuracy
B. BLEU score
C. Inception Score
D. ROUGE score
E. FID (Frechet Inception Distance)

Answer: B,D,E

Explanation:
BLEU and ROUGE are common metrics for evaluating text generation quality by comparing generated text to reference text. FID is appropriate for assessing the quality and diversity of generated images. Inception score is also relevant to asses quality and diversity but less so than FID. Accuracy isn't suitable in this context. It's more fitted to classification tasks.

NEW QUESTION # 367
......

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Valid NCA-GENM Test Dumps - NCA-GENM New Exam Materials

Latest NVIDIA Generative AI Multimodal pass review & NCA-GENM getfreedumps study materials

NVIDIA Generative AI Multimodal Sample Questions (Q362-Q367):

Valid and Reliable NCA-GENM Exam Questions [2025]

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