NCA-GENM RELIABLE TEST OBJECTIVES - DETAIL NCA-GENM EXPLANATION

NCA-GENM Reliable Test Objectives - Detail NCA-GENM Explanation

NCA-GENM Reliable Test Objectives - Detail NCA-GENM Explanation

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Tags: NCA-GENM Reliable Test Objectives, Detail NCA-GENM Explanation, Valid NCA-GENM Exam Pdf, Study NCA-GENM Plan, NCA-GENM Latest Test Questions

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NVIDIA Generative AI Multimodal Sample Questions (Q50-Q55):

NEW QUESTION # 50
You're working on a multimodal A1 model that combines audio and text to generate music. You notice that the generated music lacks musical structure and sounds random. Which of the following techniques could be applied to improve the coherence and musicality of the generated output?

  • A. Training the model on a larger dataset of music.
  • B. Adding more layers to the model.
  • C. Using a Variational Autoencoder (VAE) to learn a latent representation of musical structure.
  • D. Increasing the size of the model's hidden layers.
  • E. Using a Recurrent Neural Network (RNN) with attention mechanism to model sequential dependencies in the music.

Answer: C,E

Explanation:
A VAE can learn a structured latent space that captures essential musical features, allowing for controlled generation. RNNs with attention are well-suited for modeling sequential data like music, capturing long-range dependencies and creating a more coherent structure. Simply increasing the size or depth of the model may not address the underlying issue of musical structure. A larger dataset may help, but structured modeling techniques are generally more effective.


NEW QUESTION # 51
When experimenting with different architectures for a text-to-image model, you observe that a Diffusion model generates higher quality images than a GAN (Generative Adversarial Network). However, the Diffusion model is significantly slower to generate images. What strategy can you employ to improve the inference speed of the Diffusion model without significantly sacrificing image quality?

  • A. Increase the number of diffusion steps.
  • B. Use a larger UNet architecture within the Diffusion model.
  • C. Train the GAN for a longer duration.
  • D. Employ distillation techniques to train a faster, smaller model.
  • E. Use a smaller batch size.

Answer: D

Explanation:
Model distillation involves training a smaller, faster 'student' model to mimic the behavior of a larger, slower 'teacher' model. This allows you to retain much of the quality of the original model while significantly improving inference speed. Increasing the number of diffusion steps or using a larger UNet would further slow down the Diffusion model. Training the GAN longer doesn't address the speed issue of the Diffusion model. Using a smaller batch size might help with memory limitations, but won't significantly improve inference speed.


NEW QUESTION # 52
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. Batching Preferences
  • B. Default Model Filename
  • C. Max Queue Size
  • D. Instance Group Count
  • E. Input Data Type

Answer: D

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 # 53
You are working with a transformer-based multimodal model that processes both text and audio. You want to implement an efficient attention mechanism that reduces the computational cost associated with attending to the entire input sequence. Which of the following attention mechanisms would be MOST suitable for achieving this goal?

  • A. Global Attention
  • B. Sparse Attention
  • C. Multi-Head Attention
  • D. Local Attention
  • E. Scaled Dot-Product Attention

Answer: B

Explanation:
Sparse attention is designed to reduce the computational cost of attention by only attending to a subset of the input sequence at each position. This can significantly reduce the memory and computational requirements, making it suitable for long sequences in multimodal tasks. Other attention mechanisms (A, B, D, E) still require computing attention scores for all or most of the input sequence.


NEW QUESTION # 54
You are experimenting with a text-to-image generative model. You notice that when prompted with descriptions containing specific demographic information (e.g., 'a black doctor'), the generated images consistently reflect stereotypes. What steps can you take during the experiment evaluation phase to identify and mitigate this bias? (Select TWO)

  • A. Increase the size of the training dataset to dilute the effect of any biased examples.
  • B. Filter out all examples containing demographic information from the training dataset.
  • C. Conduct a human evaluation study where participants assess the generated images for stereotypical representations.
  • D. Randomly shuffle the training dataset to minimize bias.
  • E. Use a bias detection metric to quantify the presence of bias in the generated images, comparing output distributions across different demographic groups.

Answer: C,E

Explanation:
Bias detection metrics (B) and human evaluation (D) are essential for identifying and quantifying bias in generated content. Increasing data size (A) alone might not solve the issue. Filtering demographic information (C) can lead to underrepresentation and unfair outcomes. Random shuffling (E) does not directly address inherent biases in the training data.


NEW QUESTION # 55
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