NCA-GENM PASS4SURE QUESTIONS & NCA-GENM GUIDE TORRENT & NCA-GENM EXAM TORRENT

NCA-GENM Pass4sure Questions & NCA-GENM Guide Torrent & NCA-GENM Exam Torrent

NCA-GENM Pass4sure Questions & NCA-GENM Guide Torrent & NCA-GENM Exam Torrent

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NVIDIA Generative AI Multimodal Sample Questions (Q177-Q182):

NEW QUESTION # 177
You are evaluating a Generative A1 model for image captioning. Which of the following metrics is MOST appropriate for assessing the semantic similarity between the generated captions and the ground truth captions?

  • A. BLEU score
  • B. Perplexity
  • C. Inception Score
  • D. CIDEr score
  • E. ROUGE score

Answer: D

Explanation:
CIDEr (Consensus-based Image Description Evaluation) is specifically designed for image captioning and is highly correlated with human judgments of caption quality. While BLEU and ROUGE are useful for general text generation, CIDEr excels at capturing semantic similarity in image captions. Inception Score assesses the quality of generated images, not captions, and Perplexity measures the uncertainty of a language model.


NEW QUESTION # 178
You are working on a project involving generating photorealistic images of human faces using a generative model. Ethical considerations are paramount. Which of the following practices are MOST important to incorporate into your development workflow to mitigate potential biases and misuse?

  • A. Training the model on a diverse and representative dataset, implementing mechanisms to detect and mitigate biases in the generated images, and providing transparency about the limitations and potential risks of the technology.
  • B. Focusing solely on improving the technical performance of the model, ignoring potential ethical concerns, and releasing the model as open-source to promote innovation.
  • C. Implementing strict controls over the types of images the model can generate, limiting its use to specific applications, and restricting access to the model to a small group of trusted individuals.
  • D. Using synthetic data for training to avoid any potential privacy concerns related to real-world data, ignoring potential biases in the synthetic data, and claiming that the model is completely unbiased.
  • E. Prioritizing speed and efficiency in the development process, neglecting to address potential biases, and deploying the model without conducting thorough testing or evaluation.

Answer: A

Explanation:
Addressing ethical considerations requires a multi-faceted approach, including training on diverse data, bias detection/mitigation, and transparency. Option A encompasses all these aspects. Ignoring ethical concerns (B, D) is irresponsible. Restricting access (C) might not be feasible or effective. Synthetic data (E) can still be biased. Claiming a model is completely unbiased is misleading and incorrect.


NEW QUESTION # 179
You are tasked with optimizing a U-Net model for real-time image segmentation on an embedded device with limited GPU memory. The original model is trained in FP32 precision. Which of the following techniques, when applied together, would likely yield the best trade-off between accuracy and performance?

  • A. Converting all layers to FP16, removing skip connections from the IJ-Net architecture, and using a smaller input image resolution.
  • B. Quantization Aware Training (QAT) to INT8, Knowledge Distillation from the FP32 model to a smaller student model, and channel pruning to reduce the number of filters.
  • C. Weight clustering to reduce model size, pruning low-importance connections, and using a larger learning rate during fine-tuning.
  • D. Applying standard post-training quantization to INT8, replacing convolutional layers with fully connected layers, and using a smaller batch size.
  • E. FP16 mixed-precision training, layer fusion to combine multiple operations into one, and increasing the batch size to improve GPU utilization.

Answer: B

Explanation:
For embedded devices, aggressive optimization is needed. QAT to INT8 provides significant memory and performance gains, but requires retraining. Knowledge distillation allows training a smaller, faster student model to mimic the original model's behavior, and channel pruning reduces computational cost by removing less important filters. The combination provides the best trade-off. FP16 (B) helps but isn't as aggressive as INT8. Increasing batch size (B) might not be feasible given limited memory. Removing skip connections (D) drastically hurts accuracy. Fully connected layers (E) increase the number of parameters.


NEW QUESTION # 180
Consider a scenario where you are building a system for emotion recognition using facial expressions (images) and spoken words (audio). You plan to use a Convolutional Neural Network (CNN) for image feature extraction and a Recurrent Neural Network (RNN) for audio feature extraction. You want to combine the features learned by these networks using a cross-modal attention mechanism. Which of the following statements BEST describes how cross-modal attention can improve the performance of your system?

  • A. Cross-modal attention forces the CNN and RNN to learn identical feature representations.
  • B. Cross-modal attention reduces the computational complexity of the model by simplifying the feature extraction process.
  • C. Cross-modal attention ensures that the data from both modalities is perfectly aligned in time.
  • D. Cross-modal attention allows the model to focus on the most relevant parts of one modality based on the information from the other modality.
  • E. Cross-modal attention is only effective when the data from both modalities is perfectly synchronized.

Answer: D

Explanation:
Cross-modal attention enables the model to selectively focus on the most relevant information from one modality by attending to the cues provided by the other modality. This allows the model to learn more nuanced relationships between the modalities and improve overall performance. It doesn't force identical feature representations or reduce computational complexity. While synchronization can help, attention mechanisms can still function even without perfect temporal alignment.


NEW QUESTION # 181
You're training a conditional GAN to generate images of birds based on text descriptions. The GAN generates images, but they lack fine- grained details and often have artifacts. Which of the following techniques are MOST likely to improve the quality and realism of the generated images? (Select TWO)

  • A. Reducing the size of the input noise vector to the generator.
  • B. Implementing spectral normalization in both the generator and discriminator.
  • C. Using a deeper and wider generator network (e.g., with more layers and channels).
  • D. Using a more powerful discriminator architecture (e.g., with attention mechanisms).
  • E. Using a simple Multi-Layer Perceptron (MLP) as the generator.

Answer: B,C

Explanation:
Spectral normalization helps stabilize training by limiting the Lipschitz constant of the discriminator and generator, preventing exploding gradients and improving image quality. A deeper and wider generator network can capture more complex image features and generate more detailed images. A simple MLP wouldn't be suitable for generating high-resolution images. Reducing the input noise vector size might limit the diversity of generated images. A more powerful discriminator helps in better distinguishing between copyright images, which guides the generator to produce more realistic outputs. However, spectral normalization directly addresses stability issues that cause artifacts.


NEW QUESTION # 182
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