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NVIDIA Generative AI Multimodal Sample Questions (Q92-Q97):

NEW QUESTION # 92
You are experimenting with a text-to-image generative model. You notice that when prompted with descriptions containing specific demographic information (e.g., 'a black doctor'), the generated images consistently reflect stereotypes. What steps can you take during the experiment evaluation phase to identify and mitigate this bias? (Select TWO)

A. Increase the size of the training dataset to dilute the effect of any biased examples.
B. Use a bias detection metric to quantify the presence of bias in the generated images, comparing output distributions across different demographic groups.
C. Randomly shuffle the training dataset to minimize bias.
D. Conduct a human evaluation study where participants assess the generated images for stereotypical representations.
E. Filter out all examples containing demographic information from the training dataset.

Answer: B,D

Explanation:
Bias detection metrics (B) and human evaluation (D) are essential for identifying and quantifying bias in generated content. Increasing data size (A) alone might not solve the issue. Filtering demographic information (C) can lead to underrepresentation and unfair outcomes. Random shuffling (E) does not directly address inherent biases in the training data.

NEW QUESTION # 93
You are using NeMo to fine-tune a large language model for a specific task. You notice that the model is overfitting to the training dat a. Which of the following techniques could you apply to mitigate overfitting in this scenario? (Select all that apply)

A. Increase the size of the training dataset.
B. Add dropout layers to the model architecture.
C. Implement weight decay (L2 regularization).
D. Increase the batch size.
E. Decrease the learning rate.

Answer: A,B,C,E

Explanation:
Overfitting occurs when a model learns the training data too well and performs poorly on unseen data. Increasing the size of the training dataset provides the model with more diverse examples. Decreasing the learning rate helps the model converge to a more generalizable solution. Weight decay penalizes large weights, preventing the model from becoming too specialized to the training data. Dropout randomly disables neurons during training, forcing the model to learn more robust features. Increasing batch size can sometime prevent model to converge You are developing a system that uses a generative AI model to create personalized avatars for users based on their descriptions.

NEW QUESTION # 94
You are tasked with building a multimodal generative A1 model that takes an image and a text prompt as input and generates a corresponding audio description. The image data is processed with a Vision Transformer (ViT), the text prompt is processed with a Transformer, and you need to fuse these modalities to generate the audio. Which of the following fusion strategies would be MOST appropriate for this task, considering the need for coherent and contextually relevant audio generation?

A. Train separate models for image-to-audio and text-to-audio and then average their predicted audio features.
B. Fine-tune a pre-trained text-to-audio model using the image features as a conditioning signal.
C. Use a cross-attention mechanism where the ViT's feature maps attend to the Transformer's hidden states at multiple layers.
D. Concatenate the final hidden states of the ViT and the Transformer and feed them into a fully connected layer to generate audio features.
E. Apply a simple addition or element-wise multiplication to the final hidden states of the ViT and the Transformer.

Answer: B,C

Explanation:
Cross-attention allows the model to selectively focus on the most relevant parts of the image based on the text prompt, enabling it to generate more coherent and contextually relevant audio. Fine-tuning a pretrained text-to-audio model is a strong approach by leveraging existing knowledge of audio generation and guiding it with visual input. Simple concatenation or addition may not capture the complex relationships between modalities. Averaging predictions from separate models doesn't ensure coherence between the image and text. It is better to fine tune existing LLM models or build a fresh model from cross-attention between images and text to predict the final audio.

NEW QUESTION # 95
You have developed a multimodal model that predicts stock prices using news articles (text), historical stock data (time-series), and company financial reports (tabular data). You want to deploy this model using NVIDIA Triton Inference Server. Assume you have preprocessed the data and have individual models for each modality. What is the recommended approach to configure Triton for efficient and scalable multimodal inference?

A. Deploy the text model using ONNX Runtime, the time-series model using TensorFlow, and the tabular data model using PyTorch, and handle fusion manually.
B. Deploy each modality-specific model as a separate Triton model and use a load balancer to distribute requests across the models.
C. Deploy each modality-specific model as a separate Triton model and handle the fusion logic in the client application.
D. Create a single Triton model that encapsulates the entire multimodal pipeline, including preprocessing, individual modality models, and fusion logic, using the Ensemble Modeling feature.
E. Convert all models to TensorRT for maximum inference speed, even if it compromises accuracy due to quantization.

Answer: D

Explanation:
Using Triton's Ensemble Modeling feature (B) is the most efficient approach. It allows you to define a pipeline that includes preprocessing, individual modality models, and fusion logic within a single Triton model, simplifying deployment and management. This approach optimizes inter-model communication and reduces client-side overhead.

NEW QUESTION # 96
Consider the following code snippet used in training a multimodal model:

During experimentation, you discover that the image modality contributes negligibly to the final prediction. How would you modify the training loop to dynamically adjust the importance of each modality?

A. Introduce a modality dropout mechanism that randomly drops either the image or text modality during each training iteration.
B. Compute modality-specific gradients and apply a scaling factor to the image gradients based on their magnitude relative to the text gradients.
C. Apply a fixed weight to the image features before feeding them into the model.
D. Use a curriculum learning approach where the model is initially trained only on the text modality, and the image modality is gradually introduced.
E. Implement a separate loss function for the image modality and adjust its weight based on validation performance.

Answer: B

Explanation:
Dynamically scaling gradients based on their magnitude allows the model to automatically adjust the importance of each modality during training. If the image gradients are small compared to the text gradients, the scaling factor will increase their influence, encouraging the model to learn from the image modality. Modality dropout is helpful, however gradient scaling provides finer control.

NEW QUESTION # 97
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NVIDIA NCA-GENM NVIDIA Generative AI Multimodal Dumps - Easy To Prepare Exam [2025]

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NCA-GENM Reliable Exam Prep | Latest NCA-GENM Exam Preparation

NVIDIA - NCA-GENM –Newest Reliable Exam Prep

NVIDIA Generative AI Multimodal Sample Questions (Q92-Q97):

NVIDIA NCA-GENM NVIDIA Generative AI Multimodal Dumps - Easy To Prepare Exam [2025]

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