USEFUL NCA-GENM LABS & PASSING NCA-GENM EXAM IS NO MORE A CHALLENGING TASK

Useful NCA-GENM Labs & Passing NCA-GENM Exam is No More a Challenging Task

Useful NCA-GENM Labs & Passing NCA-GENM Exam is No More a Challenging Task

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Tags: NCA-GENM Labs, NCA-GENM Latest Dumps Questions, NCA-GENM Real Torrent, Pdf NCA-GENM Version, NCA-GENM Practice Exam Pdf

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NVIDIA Generative AI Multimodal Sample Questions (Q232-Q237):

NEW QUESTION # 232
You are deploying a multimodal model that uses both video and audio data for real-time emotion recognition. The model is deployed on an edge device with limited computational resources. Which optimization techniques would be MOST effective for reducing latency and improving the model's inference speed on the edge device?

  • A. Quantizing the model to a lower precision (e.g., INT8) and pruning less important connections.
  • B. Using full precision (FP32) for all model operations.
  • C. Increasing the resolution of the video input.
  • D. Increasing the model's complexity to improve accuracy.
  • E. Transmitting the video and audio data to a cloud server for inference.

Answer: A

Explanation:
Quantization to a lower precision (e.g., INT8) significantly reduces the model size and computational requirements, leading to faster inference speeds on edge devices. Pruning further reduces the model's complexity. Increasing model complexity (A) or using FP32 (B) would increase latency. Offloading to the cloud (D) introduces network latency. Increasing video resolution (E) increases the computational load.


NEW QUESTION # 233
You're using NVIDIA Triton to serve a multimodal model: a CLIP text encoder and a StyleGAN image generator. You need to ensure high throughput and minimal latency. Which Triton backend configuration is most suitable for this scenario, assuming both models are optimized for NVIDIA GPUs?

  • A. A single model repository containing both models as TorchScript, served by a single Triton instance using the PyTorch backend.
  • B. A Python backend where both models are loaded into memory and inference is performed sequentially.
  • C. A single model repository with two model instances (CLIP as ONNX, StyleGAN as TensorRT) served by a single Triton instance, leveraging concurrent execution.
  • D. Two separate model repositories, one for CLIP (as ONNX) and one for StyleGAN (as TensorRT), served by two Triton instances on different GPUs.
  • E. Using just the Python backend with the models on CPU.

Answer: C

Explanation:
Option C is the most efficient. Serving both models within a single Triton instance and using optimized formats (ONNX and TensorRT) allows Triton to manage resources effectively and potentially overlap computation (concurrent execution) if the models allow for it, leading to higher throughput and lower latency. Using the python backend only is less efficient than the dedicated backends. Running on different GPUs increases cost unnecessarily. TorchScript might work but depends on the models.


NEW QUESTION # 234
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. ROUGE score
  • B. Inception Score
  • C. Perplexity
  • D. BLEU score
  • E. CIDEr score

Answer: E

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 # 235
You are experimenting with different multimodal transformer architectures for a video understanding task. You are using a large pre- trained model and fine-tuning it on your specific dataset. You observe that the model is overfitting and struggling to generalize to unseen videos. Which of the following techniques would be most effective in mitigating overfitting in this scenario? (Choose two)

  • A. Use a smaller pre-trained model.
  • B. Implement weight decay and dropout regularization.
  • C. Reduce the number of transformer layers in the model.
  • D. Employ data augmentation techniques specifically designed for video data (e.g., temporal jittering, random cropping).
  • E. Increase the batch size significantly.

Answer: B,D

Explanation:
Weight decay and dropout are standard regularization techniques that help prevent overfitting. Data augmentation increases the diversity of the training data, improving the model's ability to generalize. Reducing the number of layers is a potentially viable option, but requires experimentation to achieve optimum performance.


NEW QUESTION # 236
You're working with a text-to-image generation model. After training, you notice the generated images lack fine-grained details and appear blurry. Which hyperparameter tuning strategy would be MOST effective in improving the visual quality of the generated images, considering the computational cost?

  • A. Decreasing the batch size.
  • B. Optimizing the learning rate schedule.
  • C. Switching to a different model architecture (e.g., from VAE to GAN).
  • D. Increasing the number of training epochs.
  • E. Adding more layers to the discriminator network (if using GANs).

Answer: B

Explanation:
Optimizing the learning rate schedule can have a significant impact on the quality of the generated images. A well-tuned learning rate can help the model converge to a better solution and avoid getting stuck in local minima. Increasing the number of training epochs may help, but also increases computational cost and can lead to overfitting. Adding more layers to the discriminator is a valid approach to consider if using GANs. While switching to a different architecture is an option, it would need to be justified by experimental results and may have other implications.


NEW QUESTION # 237
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