NCA-GENM ONLINE VERSION | CERTIFICATION NCA-GENM EXAM COST

NCA-GENM Online Version | Certification NCA-GENM Exam Cost

NCA-GENM Online Version | Certification NCA-GENM Exam Cost

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Tags: NCA-GENM Online Version, Certification NCA-GENM Exam Cost, NCA-GENM PDF VCE, Reliable NCA-GENM Real Exam, NCA-GENM Reliable Test Practice

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NVIDIA Generative AI Multimodal Sample Questions (Q242-Q247):

NEW QUESTION # 242
You're building a virtual assistant using NVIDIAAvatar Cloud Engine (ACE). You want the avatar to respond to user queries with realistic facial expressions and lip synchronization. Which ACE components are essential for achieving this?

  • A. Only a 3D avatar model.
  • B. Riva ASR, Riva TTS, Audi02Emotion, a 3D avatar model, and an animation engine.
  • C. Riva ASR, Riva TTS, and Audi02Emotion.
  • D. only Riva ASR and TTS.
  • E. Riva ASR, Riva TTS, Audi02Emotion, and a 3D avatar model.

Answer: B

Explanation:
A complete ACE setup for realistic avatar interaction requires: Automatic Speech Recognition (ASR) to understand the user's query, Text-to-Speech (TTS) to generate the avatar's response, Audi02Emotion to infer emotional expressions from the text/audio, a 3D avatar model to represent the avatar visually, and an animation engine to drive facial expressions and lip synchronization. This combination ensures a lifelike and engaging user experience.


NEW QUESTION # 243
A multimodal AI model, designed to translate sign language videos into text, is consistently failing on specific gestures that involve rapid hand movements. Which optimization technique targeting temporal data processing would be MOST effective to apply?

  • A. Downsample the video frames to reduce computational complexity.
  • B. Increase the batch size to improve GPU utilization during training.
  • C. Perform image segmentation to isolate the hands in each frame.
  • D. Apply frame interpolation techniques to increase the video's frame rate.
  • E. Use a recurrent neural network (RNN) or Transformer with attention mechanisms specifically designed for handling sequential data.

Answer: E

Explanation:
RNNs and Transformers, especially those with attention, are designed to capture temporal dependencies in sequential data like video. Increasing frame rate (B) might help slightly, but doesn't address the fundamental issue of modeling sequential data effectively. The other options are either irrelevant or detrimental to capturing the rapid hand movements. RNNs/Transformers capture the movement itself, which is key here. Image segmentation could be a preprocessing step, but RNN/Transformer is the core optimization.


NEW QUESTION # 244
You're building a system to translate speech to text using an encoder-decoder architecture with attention. You observe that the translated text often repeats phrases from the input speech. Which regularization techniques could help mitigate this issue? (Select TWO)

  • A. Decreasing the number of attention heads.
  • B. Increasing the size of the vocabulary.
  • C. Applying label smoothing to the target sequences.
  • D. Adding L1 regularization to the embedding layer.
  • E. Adding dropout to the encoder and decoder layers.

Answer: C,E

Explanation:
Dropout prevents overfitting by randomly dropping neurons during training, forcing the network to learn more robust representations and reducing reliance on specific features. Label smoothing encourages the model to be less confident in its predictions, making it less likely to overfit to the training data and repeat phrases. The size of the vocabulary, the number of attention heads, and L1 regularization on embeddings are less directly related to preventing repetition.


NEW QUESTION # 245
You are fine-tuning a pre-trained multimodal model for a new task. You have limited computational resources. Which of the following fine-tuning strategies would be the MOST computationally efficient while still achieving good performance?

  • A. Fine-tune all the layers of the model.
  • B. Train a new random model from scratch for the task, which will avoid the need to load the pre-trained model.
  • C. Freeze all layers except the classification head and fine-tune only the classification head.
  • D. Randomize the model to train, if it improves the training rate.
  • E. Freeze the lower layers of the model and fine-tune the upper layers and the classification head.

Answer: E

Explanation:
Freezing the lower layers and fine-tuning the upper layers and classification head strikes a balance between computational efficiency and performance. The lower layers typically capture more general features that are less specific to the task, while the upper layers capture more task-specific features. Freezing the lower layers reduces the number of trainable parameters, making the fine-tuning process more computationally efficient. Fine-tuning all layers is computationally expensive, freezing all layers except the classification head might not be sufficient for adapting to the new task, and training from scratch does not leverage the knowledge learned during pre-training. Randomizing model is not a general practice.


NEW QUESTION # 246
You are building a system that uses text and images to generate 3D models. The text describes the object, and the images provide visual details. During training, you observe that the model heavily relies on the image input and largely ignores the text description. What technique can you employ to encourage the model to give more weight to the textual input?

  • A. Decrease the learning rate for the image processing branch of the model.
  • B. Increase the size of the text vocabulary.
  • C. Increase the resolution of the input images.
  • D. Use a curriculum learning approach, starting with simpler text descriptions and gradually increasing the complexity.
  • E. Apply a higher dropout rate to the image embedding layer.

Answer: D,E

Explanation:
Explanation:A
Applying a higher dropout rate (B) to the image embedding forces the model to rely less on the image features. Curriculum learning (E) allows the model to first learn to associate simpler text descriptions with corresponding visual features, then gradually more complex descriptions are introduced.


NEW QUESTION # 247
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