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Updated NVIDIA - NCA-GENM - New NVIDIA Generative AI Multimodal Exam Price

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NVIDIA Generative AI Multimodal Sample Questions (Q156-Q161):

NEW QUESTION # 156
Which of the following are key benefits of using multimodal learning compared to unimodal learning? (Select TWO correct answers)

A. Enhanced ability to capture complex relationships between different data types.
B. Guaranteed perfect accuracy.
C. Reduced computational complexity.
D. Simpler model architectures.
E. Improved robustness to noise and missing data in one modality.

Answer: A,E

Explanation:
Multimodal learning leverages information from multiple modalities, which can lead to improved robustness because the model can rely on other modalities when one is noisy or incomplete. It also allows the model to learn more complex relationships that might not be apparent from a single modality.

NEW QUESTION # 157
You are training a conditional generative model to generate images based on text descriptions. You notice that the generated images often lack fine-grained details and tend to be blurry, even though the overall structure matches the text description. Which of the following techniques would be MOST effective in improving the image quality and adding finer details?

A. Train the model for fewer epochs.
B. Implement a perceptual loss function that compares high-level features of generated and real images.
C. Use a simpler generator architecture.
D. Decrease the learning rate of the discriminator.
E. Increase the batch size used for training.

Answer: B

Explanation:
Perceptual loss functions, which often use features extracted from pre-trained convolutional neural networks, can guide the generator to produce images that are more visually realistic and contain finer details. They help the model capture high-level features and style information that are important for image quality. Increasing batch size, simplifying the generator, decreasing discriminator learning rate, or training for fewer epochs are unlikely to directly address the issue of missing fine-grained details.

NEW QUESTION # 158
You are working on a project that involves generating realistic images from text descriptions using a diffusion model. You want to reduce the inference time of the model, which currently takes several minutes to generate a single image. Which of the following techniques would be MOST effective for accelerating inference without significantly compromising image quality?

A. Increasing the number of diffusion steps.
B. Training the diffusion model with a larger dataset.
C. Switching to a CPU-based inference engine.
D. Employing techniques like DDIM (Denoising Diffusion Implicit Models) or progressive distillation to reduce the number of sampling steps required.
E. Using a smaller batch size during inference.

Answer: D

Explanation:
DDIM and progressive distillation are specifically designed to reduce the number of sampling steps needed in diffusion models, leading to faster inference. Increasing diffusion steps (A) would increase inference time. Smaller batch size (B) might reduce memory usage, but not significantly affect overall inference time. Training with a larger dataset (D) improves quality, but not inference speed. Switching to CPU (E) would dramatically slow down inference.

NEW QUESTION # 159
You're training a multimodal Generative A1 model that takes video and text as input to predict future frames of the video. You notice that the model generates plausible visual content but often fails to accurately reflect the actions described in the text. Which of the following techniques is MOST likely to improve the alignment between the generated video and the text description?

A. Increase the frame rate of the training videos.
B. Decrease the resolution of the video frames.
C. Implement a contrastive learning objective that encourages similar embeddings for corresponding video frames and text descriptions.
D. Using only pretrained model weights.
E. Use a larger vocabulary for the text encoder.

Answer: C

Explanation:
Contrastive learning directly encourages the model to learn a shared representation space where semantically similar video frames and text descriptions are close to each other, improving alignment. Increasing frame rate, vocabulary size, or decreasing video resolution will not directly address the alignment problem. Training the whole model is needed instead of using just pre-trained weights.

NEW QUESTION # 160
You're building a multimodal model that predicts customer satisfaction based on their written reviews and associated call center audio recordings. You've pre-trained separate text and audio encoders. What's the MOST effective strategy to fuse these modalities for the final prediction task?

A. Average the output embeddings of both encoders element-wise.
B. Train a separate attention mechanism to weigh the contributions of each modality before concatenation.
C. Fine-tune only the text encoder, keeping the audio encoder frozen.
D. Concatenate the final hidden states from both encoders and feed them into a fully connected layer.
E. Add the hidden states of both encoders element-wise.

Answer: B

Explanation:
An attention mechanism allows the model to dynamically learn the importance of each modality based on the input. This is more flexible and effective than simple concatenation or averaging (A, B, E), which treat both modalities equally. Fine-tuning only one encoder (C) doesn't leverage the benefits of multimodal fusion. While concatenation (A) is a common starting point, attention provides a more nuanced and powerful way to combine modalities. Adding features (E) is also not effective.

NEW QUESTION # 161
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