NCA-GENM VALID EXAM SIMS & LATEST NCA-GENM DUMPS FREE

NCA-GENM Valid Exam Sims & Latest NCA-GENM Dumps Free

NCA-GENM Valid Exam Sims & Latest NCA-GENM Dumps Free

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NVIDIA Generative AI Multimodal Sample Questions (Q126-Q131):

NEW QUESTION # 126
Consider a scenario where you're building a multimodal model to generate image captions. You've pre-trained a large language model (LLM) on a massive text corpus and a convolutional neural network (CNN) on ImageNet. How would you effectively combine these pre- trained components for your image captioning task, considering the need to maintain high caption quality and training efficiency?

  • A. Freeze the LLM, train the CNN to predict text embeddings, and then decode these embeddings into captions.
  • B. Use a transformer-based encoder to process both image features and text embeddings before feeding them to the LLM decoder.
  • C. Fine-tune both the CNN and the LLM jointly on the image captioning dataset.
  • D. Freeze the CNN, extract image features, and train the LLM to generate captions from these features.
  • E. Train the CNN and LLM separately on unrelated datasets and then combine them at inference time using a simple averaging of their outputs.

Answer: B,C

Explanation:
Fine-tuning both the CNN and LLM jointly allows the model to adapt both visual feature extraction and language generation to the specific task of image captioning, leading to potentially higher quality captions. However, this can be computationally expensive. Using a transformer-based encoder to process both modalities before the LLM decoder allows for effective cross-modal attention and fusion, which is also a strong approach. Freezing either the CNN or LLM limits the model's ability to adapt. Training separately and averaging outputs is unlikely to produce coherent captions.


NEW QUESTION # 127
When training a multimodal generative model for image captioning, you notice the model generates grammatically correct but generic and uninformative captions. Which technique is MOST likely to improve the in formativeness and specificity of the generated captions?

  • A. Employ a diverse beam search or sampling strategy during inference to encourage exploration of different caption possibilities.
  • B. Use beam search during inference with a large beam size.
  • C. Increase the size of the image encoder.
  • D. Decrease the learning rate during training.
  • E. Decrese the size of the vocabulary.

Answer: A

Explanation:
Diverse beam search or sampling strategies encourage the model to explore different caption possibilities during inference, leading to more diverse and informative captions. Standard beam search often converges to the most likely caption, which tends to be generic. Increasing the image encoder Size might improve image feature extraction but doesn't directly address the caption informativeness problem. Decreasing the learning rate is a general training technique that might improve convergence but doesn't specifically target caption informativeness.


NEW QUESTION # 128
Which of the following is NOT a typical application or benefit of using U-Net architectures in generative AI, particularly within the context of image generation and manipulation?

  • A. Image segmentation and pixel-wise classification.
  • B. Facilitating efficient feature extraction and upsampling for detailed image generation.
  • C. Encoding high-dimensional text data for multimodal embeddings.
  • D. Image inpainting and super-resolution tasks.
  • E. Medical image analysis, such as tumor detection.

Answer: C

Explanation:
U-Nets are primarily used for image-to-image tasks like segmentation, inpainting, and super-resolution. They excel at processing and generating images, but are not directly involved in encoding text data. CLIP is used for that purpose.


NEW QUESTION # 129
Consider this Python code snippet using PyTorch:

  • A. Error. The transpose operation is incorrect for achieving cross-modal attention.
  • B. torch.Size([256, 512]). This implementation is correct and efficient for cross-modal attention.
  • C.
  • D. torch.Size ([32, 32]). This is correct and computes attention weights for each text-image pair in the batch independently
  • E. torch.Size ([32, 5121). The issue is a dimension mismatch.

Answer: C

Explanation:
The shape of the 'attention' tensor is torch.Size([32, 32]). The matrix multiplication of (32, 256) with (512, 32) results in a (32, 32) tensor. The crucial issue here is the batch-wise attention calculation. The attention weights are being computed between all text embeddings and all image embeddings in the batch. During training, this leads to 'information leakage' because the model is learning relationships between samples that shouldn't be related (i.e., different text-image pairs in the batch are influencing each other). For proper cross-modal attention, you would typically want to compute the attention weights only between corresponding text and image embeddings within the same sample.


NEW QUESTION # 130
You are building a system that uses audio and video to detect emotional states of a user. What are the challenges to this system?

  • A. Synchronization issues between audio and video streams.
  • B. Differences in lighting conditions influencing facial expression recognition.
  • C. All of the above.
  • D. Subjectivity in emotional expression across cultures and individuals.
  • E. Variations in background noise affecting audio quality.

Answer: C

Explanation:
All the options pose challenges. Background noise affects audio analysis, varying lighting impairs facial recognition, subjective emotional expression necessitates robust models, and synchronization issues hinder accurate multimodal integration.


NEW QUESTION # 131
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