NCA-GENM TRAINING ONLINE: NVIDIA GENERATIVE AI MULTIMODAL & NVIDIA GENERATIVE AI MULTIMODAL DUMPS TORRENT

NCA-GENM Training Online: NVIDIA Generative AI Multimodal & NVIDIA Generative AI Multimodal Dumps Torrent

NCA-GENM Training Online: NVIDIA Generative AI Multimodal & NVIDIA Generative AI Multimodal Dumps Torrent

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

NEW QUESTION # 177
You are working on a generative A1 model that creates descriptions of images. During experimentation, you notice the model consistently generates descriptions that are factually incorrect about objects in the image, despite the image quality being high. For example, it might describe a 'cat' as a 'dog'. What is the MOST critical step to address this issue?

  • A. Increase the training data size with more diverse images.
  • B. Implement a mechanism to verify the generated descriptions against an external knowledge base or object recognition system.
  • C. Use a more complex model architecture.
  • D. Fine-tune the model using a smaller learning rate.
  • E. Apply image sharpening filters to the input images.

Answer: B

Explanation:
Factually incorrect descriptions indicate a lack of grounding in real-world knowledge. Verifying against an external knowledge base (B) directly addresses this issue. Increasing data size (A) might help, but it's not guaranteed. Fine-tuning (C) and increasing model complexity (D) might not solve the grounding problem. Image sharpening (E) is irrelevant to factual accuracy.


NEW QUESTION # 178
You are building a multimodal generative A1 model that combines text, images, and audio. You notice that the model performs well on text and images but struggles with audio, particularly in noisy environments. Which of the following strategies would be MOST effective in improving the model's performance with audio data?

  • A. Use transfer learning by pre-training the audio component of the model on a large audio dataset.
  • B. Increase the learning rate for the audio modality during training.
  • C. Decrease the weight of the audio modality in the loss function.
  • D. Reduce the dimensionality of the audio features to simplify the learning task.
  • E. Apply data augmentation techniques specifically designed for audio, such as adding noise or varying the speed and pitch.

Answer: A,E

Explanation:
Data augmentation (C) increases the robustness of the model to variations in audio, including noise. Transfer learning (E) allows the model to leverage knowledge from a large, pre-existing audio dataset, improving its initial performance.


NEW QUESTION # 179
You are deploying a Riva-based speech-to-text service in a production environment. You observe high latency and CPU utilization on your server Which of the following actions would be most effective in optimizing the performance of your Riva service?

  • A. Disabling automatic punctuation and capitalization to simplify the ASR process.
  • B. Increasing the audio chunk size sent to the Riva server to reduce the number of requests.
  • C. Switching to a smaller, less accurate ASR model to reduce computational load.
  • D. Deploying the Riva server on a CPU-only instance to reduce cost.
  • E. Enabling batching and concurrency in the Riva server configuration to process multiple requests simultaneously.

Answer: E

Explanation:
Enabling batching and concurrency is a key optimization strategy for Riva. It allows the server to process multiple audio streams simultaneously, maximizing GPU utilization and reducing overall latency. Switching to a smaller model (A) might reduce load but also decreases accuracy. Disabling punctuation (C) has a minor impact. Increasing audio chunk size (D) can help, but batching is more significant. Deploying on CPU (E) negates the benefits of Riva's GPU acceleration.


NEW QUESTION # 180
You are building a multimodal model for medical image diagnosis, using both radiology images (e.g., X-rays) and patient clinical notes.
The clinical notes are highly unstructured and contain significant medical jargon. What preprocessing steps would be MOST effective for improving the model's performance?

  • A. Performing sentiment analysis on the clinical notes.
  • B. Directly feeding the raw clinical notes into the model without any preprocessing.
  • C. Translating the clinical notes into multiple languages and then back-translating to the original language.
  • D. Utilizing named entity recognition (NER) to identify medical entities (diseases, medications, etc.), and employing a medical-specific language model (e.g., BioBERT) for text embeddings.
  • E. Applying basic text cleaning (removing punctuation, converting to lowercase) and using a standard word embedding (e.g., Word2Vec).

Answer: D

Explanation:
For medical text, NER and BioBERT are the best choice. NER extracts relevant medical information, while BioBERT is pre-trained on a large corpus of biomedical text, allowing it to better understand medical jargon and context. Raw text (A) would be ineffective. Basic cleaning and Word2Vec (B) are insufficient for complex medical language. Translation (D) isn't the primary preprocessing step needed. Sentiment analysis (E) is You have a multimodal generative model that produces images from text descriptions.


NEW QUESTION # 181
You are building a multimodal model that takes video and audio as input. You want to fuse the information extracted from both modalities. Which of the following fusion techniques allows for learning temporal dependencies between modalities?

  • A. Late Fusion (averaging the probabilities from separate networks).
  • B. Attention-based Fusion using Transformers, allowing the model to weigh the importance of different parts of each modality over time.
  • C. Maximum pooling across feature vectors from video and audio streams.
  • D. Early Fusion (concatenating features before feeding into a single network).
  • E. Simple Addition of feature vectors from video and audio streams.

Answer: B

Explanation:
Attention-based Fusion, particularly using Transformers, is well-suited for capturing temporal dependencies in multimodal data. Transformers can learn which parts of each modality are most relevant at different points in time, enabling a more nuanced fusion of information. Early Fusion (A) fuses features statically and doesn't capture temporal dependencies directly. Late Fusion (B) also struggles to capture fine- grained temporal relationships. Simple addition (D) and max pooling (E) are too simplistic to model complex temporal interactions.


NEW QUESTION # 182
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