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NVIDIA Generative AI Multimodal Sample Questions (Q106-Q111):

NEW QUESTION # 106
You are building a multimodal generative A1 model that creates realistic indoor scenes by combining textual descriptions, floor plans (geospatial data), and object libraries. The goal is to generate high-quality 3D models of the scenes. However, the model often produces scenes with physically implausible object arrangements (e.g., objects floating in the air, overlapping furniture). How can you MOST effectively integrate physical constraints into the generation process to ensure more realistic scene compositions?

A. Implement a rule-based system that enforces basic physical constraints (e.g., objects must be supported by a surface, no object interpenetration) during the generation process.
B. Increase the size of the training dataset with more examples of realistic indoor scenes.
C. Use a physics engine (e.g., NVIDIA PhysX) as a post-processing step to simulate the generated scene and correct any physically implausible object placements.
D. Train a separate discriminator network that evaluates the physical plausibility of generated scenes and penalizes implausible configurations during training.
E. Force the model to generate only scenes that exist within the training set.

Answer: A,C,D

Explanation:
Using a physics engine for post-processing (B) directly simulates physical interactions. Implementing a rule-based system (C) enforces basic constraints. Training a discriminator (D) adds a learning component for physical plausibility. Increasing the dataset size (A) might help but doesn't guarantee physical plausibility. Limiting generation to the training set (E) restricts creativity and generalization.

NEW QUESTION # 107
You are building a multimodal model that takes video and audio as input. You want to fuse the information extracted from both modalities. Which of the following fusion techniques allows for learning temporal dependencies between modalities?

A. Late Fusion (averaging the probabilities from separate networks).
B. Maximum pooling across feature vectors from video and audio streams.
C. Attention-based Fusion using Transformers, allowing the model to weigh the importance of different parts of each modality over time.
D. Early Fusion (concatenating features before feeding into a single network).
E. Simple Addition of feature vectors from video and audio streams.

Answer: C

Explanation:
Attention-based Fusion, particularly using Transformers, is well-suited for capturing temporal dependencies in multimodal data. Transformers can learn which parts of each modality are most relevant at different points in time, enabling a more nuanced fusion of information. Early Fusion (A) fuses features statically and doesn't capture temporal dependencies directly. Late Fusion (B) also struggles to capture fine- grained temporal relationships. Simple addition (D) and max pooling (E) are too simplistic to model complex temporal interactions.

NEW QUESTION # 108
Consider a scenario where you're integrating CLIP with a generative model to create images from text prompts. Which of the following best describes the primary role of CLIP in this process?

A. To optimize the hyperparameters of the generative model.
B. To encode text prompts into a vector representation that guides the image generation process.
C. To directly generate images based on text prompts.
D. To act as a discriminator in a GAN setup.
E. To decode generated images back into text descriptions.

Answer: B

Explanation:
CLIP (Contrastive Language-Image Pre-training) serves as an encoder to map text prompts into a vector space. This vector representation is then used to guide the generative model towards creating images that align with the semantic meaning of the text prompt. CLIP doesn't generate images directly, decode images to text or optimize hyperparameters.

NEW QUESTION # 109
You are using NeMo to fine-tune a large language model for a text-to-image task. During training, you encounter a CUDA out-of-memory error, despite using mixed-precision training. What is the MOST effective strategy to reduce memory consumption and continue training without significantly degrading model performance?

A. Switch to a smaller model architecture.
B. Decrease the gradient accumulation steps.
C. Disable mixed-precision training.
D. Enable gradient checkpointing.
E. Increase the batch size.

Answer: D

Explanation:
Gradient checkpointing (also known as activation recomputation) trades compute for memory. It avoids storing all intermediate activations during the forward pass, instead recomputing them during the backward pass. This significantly reduces memory footprint, allowing you to train larger models or use larger batch sizes. Increasing batch size increases memory consumption. Decreasing gradient accumulation steps might help slightly but is less effective than gradient checkpointing. Switching to a smaller model or disabling mixed precision would degrade model performance.

NEW QUESTION # 110
Consider a multimodal dataset containing patient records: text descriptions of symptoms, MRI images, and audio recordings of heart sounds. Some records are missing MRI images. Which of the following methods is BEST suited for handling this missing data within a multimodal learning framework?

A. Imputing missing MRI images using the average MRI image from the entire dataset.
B. Using a masking approach during training, where the model is trained to predict the missing modality (MRI) from the available modalities (text and audio) for incomplete records and is trained with all modalities for complete records.
C. Ignoring the MRI data completely and training the model only on the text and audio data.
D. Training a separate model only on records with complete data and then using it to predict the missing data.
E. Deleting all records with missing MRI images.

Answer: B

Explanation:
Masking provides a way to leverage the available information in all records, even those with missing modalities. The model learns to infer the missing data from the available data, which can improve overall performance. Deleting data or using a simple average imputation can introduce bias or information loss. Training a separate model or ignoring the MRI data altogether does not effectively utilize the available multimodal information for all records.

NEW QUESTION # 111
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