FREE PDF QUIZ HIGH PASS-RATE NVIDIA - NCA-GENM VALID TEST BLUEPRINT

Free PDF Quiz High Pass-Rate NVIDIA - NCA-GENM Valid Test Blueprint

Free PDF Quiz High Pass-Rate NVIDIA - NCA-GENM Valid Test Blueprint

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Tags: NCA-GENM Valid Test Blueprint, Exam NCA-GENM Study Solutions, NCA-GENM Testking, Reliable NCA-GENM Exam Pdf, NCA-GENM Useful Dumps

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NVIDIA Generative AI Multimodal Sample Questions (Q45-Q50):

NEW QUESTION # 45
You are tasked with deploying a generative A1 model using NVIDIA Triton Inference Server. Which configuration parameter within Triton is MOST crucial for optimizing throughput and minimizing latency when serving a large number of concurrent requests?

  • A. Max Queue Size
  • B. Batching Preferences
  • C. Default Model Filename
  • D. Input Data Type
  • E. Instance Group Count

Answer: E

Explanation:
The 'Instance Group Count' parameter in Triton determines how many instances of the model are loaded onto the GPU(s) and/or CPU(s). Increasing the number of instances (up to the hardware's capacity) allows Triton to handle more concurrent requests in parallel, thereby improving throughput and reducing latency. While batching and max queue size can also help, the instance count is the most fundamental for parallelism. The default model filename is irrelevent to performance and input data type is a requirement not a performance consideration.


NEW QUESTION # 46
Which of the following is NOT a common challenge in training multimodal Generative AI models?

  • A. Handling different data modalities with varying statistical properties.
  • B. Dealing with missing modality data during inference.
  • C. Aligning feature spaces of different modalities.
  • D. Optimizing for a single modality at the expense of others.
  • E. The computational complexity associated with training large unimodal models.

Answer: E

Explanation:
The computational complexity of training large unimodal models is a challenge for unimodal models, but not a distinct challenge inherent to multimodal models. Multimodal models have unique challenges related to data heterogeneity, feature alignment, handling missing modalities, and balancing performance across modalities.


NEW QUESTION # 47
Which of the following are potential benefits of using multi-modal learning compared to single-modal learning? (Select all that apply)

  • A. Reduced risk of overfitting to spurious correlations in a single modality.
  • B. Guaranteed higher accuracy across all tasks.
  • C. Increased computational complexity and data requirements.
  • D. The ability to learn more comprehensive and nuanced representations.
  • E. Improved robustness to noisy or incomplete data.

Answer: A,D,E

Explanation:
Multi-modal learning leverages the complementary information from different modalities to enhance performance. (A) It improves robustness because if one modality is noisy or missing, the others can still provide useful information. (B) It learns more comprehensive representations by integrating information across modalities. (D) It reduces overfitting by leveraging information from multiple sources. (C) is correct but not a benefit. (E) is incorrect as higher accuracy is not guaranteed, depending on data and task.


NEW QUESTION # 48
You're developing a system to generate realistic 3D models from text descriptions. You're using a diffusion model-based approach and find that the generated models often lack fine details and exhibit artifacts. Which of the following techniques would likely lead to the MOST significant improvement in the quality of the generated 3D models?

  • A. Implement classifier-free guidance with a higher guidance scale.
  • B. Train the diffusion model on a larger dataset of text-3D model pairs.
  • C. Use a larger IJ-Net architecture for the denoising process.
  • D. All of the above
  • E. Increase the number of diffusion steps during the reverse diffusion process.

Answer: D

Explanation:
Each of the above options address the lack of fine details and exhibit artifacts. Increasing diffusion steps lets the model refine the results. Using a larger U-Net architecture increases capacity of details. Classifier-free guidance allows for generating high fidelity details by better correlating text descriptions. Training on a larger dataset enables richer context. Overall improvements allow for finer details with fewer artifacts


NEW QUESTION # 49
You are building a multimodal Generative AI system to generate marketing content. You have text descriptions of products, images of the products, and customer reviews. Which of the following strategies would best handle potential inconsistencies or contradictions between these different modalities?

  • A. Using a simple averaging method to combine the features from each modality.
  • B. Ignoring customer reviews as they are often unreliable.
  • C. Prioritizing the image data as images are the most visually appealing and engaging.
  • D. Training separate models for each modality and then averaging the outputs.
  • E. Employing an attention mechanism or a cross-modal fusion network that learns to weigh the importance of each modality based on the context.

Answer: E

Explanation:
Employing an attention mechanism or a cross-modal fusion network allows the model to dynamically learn which modalities are most relevant for a given input or generation task. This helps in resolving inconsistencies by giving more weight to reliable modalities and diminishing the influence of less reliable ones. Simple averaging or prioritization can lead to suboptimal results when modalities contradict each other.


NEW QUESTION # 50
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