Lab NCA-GENM Questions & NCA-GENM Training Solutions

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

NEW QUESTION # 31
You're building a text generation model using a Transformer architecture. You observe that the generated text often gets stuck in repetitive loops, producing the same phrase over and over. Which of the following strategies is MOST likely to mitigate this issue?

A. Increase the temperature parameter during text generation.
B. Increase the number of attention heads in the Transformer.
C. Use a smaller vccabulary size.
D. Decrease the learning rate of the model during training.
E. Implement beam search with a larger beam width.

Answer: A

Explanation:
Increasing the temperature parameter introduces more randomness into the sampling process during text generation. This makes the model less likely to repeatedly select the same high-probability token, thus reducing repetitive loops. Decreasing the learning rate or using a smaller vocabulary are unlikely to solve the repetition problem directly Beam search can sometimes amplify repetition, and increasing the number of attention heads primarily affects model capacity, not repetition.

NEW QUESTION # 32
You are training a multimodal generative A1 model for image captioning. After initial training, you observe that the model excels at describing common objects but struggles with nuanced details and rare objects. Which of the following performance optimization strategies would be MOST effective in addressing this issue?

A. Implement a custom loss function that penalizes inaccuracies in describing rare objects more heavily.
B. Apply early stopping to prevent overfitting to the common objects.
C. Reduce the learning rate to fine-tune the model on the existing dataset.
D. Increase the batch size during training to improve GPU utilization.
E. Increase the number of layers in the encoder network.

Answer: A

Explanation:
Implementing a custom loss function is the most effective strategy because it directly addresses the model's weakness by focusing on accurate descriptions of rare objects. Increasing batch size improves training speed but not necessarily accuracy. Early stopping prevents overfitting, but doesn't specifically target the issue of rare object recognition. Reducing the learning rate might help with fine-tuning, but not as effectively as a targeted loss function. Increasing the number of layers may increase complexity but not guarantee better performance on rare objects.

NEW QUESTION # 33
You're working with a multimodal model that fuses text and image features. You've noticed that the model performs poorly when the text and image are semantically misaligned (e.g., an image of a dog and the caption 'a cat on a mat'). Which of the following techniques can help improve the model's robustness to such misalignment?

A. Adding a contrastive loss that penalizes embeddings of misaligned text-image pairs.
B. Increasing the learning rate during training.
C. Removing dropout layers from the model architecture.
D. Using only positive text-image pairs for training.
E. Decreasing the batch size.

Answer: A

Explanation:
A contrastive loss function directly addresses the issue of semantic misalignment by penalizing the model when it produces similar embeddings for text and images that don't correspond semantically. This encourages the model to learn more robust and meaningful feature representations.

NEW QUESTION # 34
You're building a multimodal model that predicts customer satisfaction based on their written reviews and associated call center audio recordings. You've pre-trained separate text and audio encoders. What's the MOST effective strategy to fuse these modalities for the final prediction task?

A. Average the output embeddings of both encoders element-wise.
B. Concatenate the final hidden states from both encoders and feed them into a fully connected layer.
C. Train a separate attention mechanism to weigh the contributions of each modality before concatenation.
D. Fine-tune only the text encoder, keeping the audio encoder frozen.
E. Add the hidden states of both encoders element-wise.

Answer: C

Explanation:
An attention mechanism allows the model to dynamically learn the importance of each modality based on the input. This is more flexible and effective than simple concatenation or averaging (A, B, E), which treat both modalities equally. Fine-tuning only one encoder (C) doesn't leverage the benefits of multimodal fusion. While concatenation (A) is a common starting point, attention provides a more nuanced and powerful way to combine modalities. Adding features (E) is also not effective.

NEW QUESTION # 35
You have a multimodal model combining video and text data for action recognition. The model performs well on standard datasets but struggles with videos containing unusual camera angles or lighting conditions. Which data augmentation strategy would be MOST effective in improving the model's robustness?

A. Applying random rotations, flips, and color jittering to the video frames.
B. Adding random noise to the audio track.
C. Replacing random words in the text descriptions with synonyms.
D. Randomly cropping and scaling the video frames.
E. Reducing the frame rate of the videos.

Answer: A

Explanation:
Applying random rotations, flips, and color jittering directly addresses the model's sensitivity to camera angles and lighting conditions by exposing it to a wider range of visual variations during training. The other options are less relevant to these specific issues. Audio noise is irrelevant to visual robustness. Cropping and scaling are basic augmentations but less effective than transformations that simulate camera angles and lighting. Synonym replacement improves text understanding, but not visual robustness. Reducing frame rate can reduce computation but doesn't improve robustness to visual variations.

NEW QUESTION # 36
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Lab NCA-GENM Questions & NCA-GENM Training Solutions

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

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