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NVIDIA Generative AI Multimodal Sample Questions (Q352-Q357):

NEW QUESTION # 352
You are tasked with building a system that generates realistic images from text descriptions. Which of the following loss functions is MOST crucial for ensuring the generated images are both visually appealing and semantically aligned with the text?

A. Mean Squared Error (MSE) between the generated image and the target image (if available).
B. Binary Cross-Entropy loss for discriminating between real and generated images (used in GANs).
C. A contrastive loss that encourages similar text and image embeddings to be closer together in a shared embedding space.
D. A perceptual loss that measures the difference in high-level features extracted by a pre-trained convolutional neural network.
E. All of the above.

Answer: E

Explanation:
Option E is the best answer. All the mentioned loss functions play a vital role. GANs use Binary Cross-Entropy for realistic image generation. Perceptual Loss helps in creating more visually appealing images, and Contrastive Loss ensures that the image aligns with the text description by projecting them into a common embedding space.

NEW QUESTION # 353
You have a text-to-image model deployed using Triton Inference Server. You want to monitor the GPU utilization and inference latency to ensure optimal performance. Which of the following methods is the MOST effective way to achieve this?

A. Using the Triton Inference Server client API to measure inference latency from the client-side.
B. Relying solely on the operating system's resource monitor to track GPU usage.
C. Writing custom scripts to parse Triton's log files and extract performance metrics.
D. Using 'nvidia-smi' to periodically check GPU utilization and manually calculate latency.
E. Using Triton's built-in Prometheus metrics endpoint and Grafana for visualization.

Answer: E

Explanation:
Triton Inference Server exposes a Prometheus metrics endpoint that provides detailed information about GPIJ utilization, inference latency, and other performance metrics. Prometheus is a popular time-series database and monitoring solution. Grafana can then be used to visualize these metrics in real-time dashboards. This is the recommended approach for monitoring Triton deployments.

NEW QUESTION # 354
You are developing a multimodal sentiment analysis model that combines text reviews and product images. You observe that the model's performance is significantly better when only text is used, compared to when both text and images are combined. What are the potential reasons for this performance degradation, and how can you address them effectively? (Choose two)

A. The model is overfitting to the image features.
B. The model is not properly aligning the text and image features, leading to conflicting signals.
C. The text encoder is too complex, hindering the model's ability to process image information.
D. The image features are irrelevant to the sentiment expressed in the text.
E. The image features are noisy or of poor quality, confusing the model.

Answer: B,E

Explanation:
Poor quality or noisy image features can confuse the model. Incorrect alignment between text and image features leads to conflicting signals, hindering the model's ability to learn a cohesive representation. Irrelevant image features, overfitting and text encoder complexity are all potential reasons that are less likely than the chosen answers.

NEW QUESTION # 355
Consider the following code snippet used within a U-Net architecture. What is its purpose?
torch.cat ([up, skip], dim=1)

A. It performs a matrix multiplication between the 'up' and 'skip' tensors.
B. It performs an element-wise addition of the 'up' and 'skip' tensors.
C. It subtracts the 'skip' tensor from the 'up' tensor.
D. It concatenates the 'up' and 'skip' tensors along the channel dimension.
E. It multiplies the 'up' and 'skip' tensors element-wise.

Answer: D

Explanation:
The 'torch.cat([up, skip], dim=1) function concatenates two tensors, 'up' and 'skip' , along the channel dimension (dim=1) In the context of a U-Net, 'up' represents the upsampled feature map from the decoder path, and 'skip' represents the corresponding feature map from the encoder path. Concatenating them allows the decoder to combine both coarse-grained and fine-grained information for better image reconstruction.

NEW QUESTION # 356
You are building a multimodal model for medical diagnosis that combines patient medical history (text), medical images (X-rays, MRIs), and sensor data (heart rate, blood pressure). The dataset contains significant amounts of missing data across all modalities. What strategy is most appropriate for handling the missing data and ensuring the model's robustness and accuracy?

A. Training seperate models for each avalible modality.
B. Using a multimodal variational autoencoder (MVAE) to learn a joint latent representation of the data and impute missing values based on the observed modalities.
C. Using a Generative Adversarial Network(GAN) to impute missing values based on the other avalible modalities.
D. Removing all patients with missing data to create a clean dataset.
E. Imputing missing values using simple methods like mean imputation or filling with a constant value.

Answer: B,C

Explanation:
Removing patients with missing data can lead to a significant loss of information and bias the model. Simple imputation methods can introduce inaccuracies and fail to capture the relationships between modalities. Multimodal variational autoencoders (MVAEs) are specifically designed to handle missing data in multimodal datasets by learning a joint latent representation and imputing values based on the observed modalities. This approach is more robust and accurate than simple imputation methods. GAN can also be used to impute missing values.

NEW QUESTION # 357
......

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Ken Shaw Ken Shaw

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