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NVIDIA Generative AI Multimodal Sample Questions (Q155-Q160):

NEW QUESTION # 155
You're training a conditional GAN to generate images of birds based on text descriptions. The GAN generates images, but they lack fine- grained details and often have artifacts. Which of the following techniques are MOST likely to improve the quality and realism of the generated images? (Select TWO)

A. Using a deeper and wider generator network (e.g., with more layers and channels).
B. Reducing the size of the input noise vector to the generator.
C. Using a simple Multi-Layer Perceptron (MLP) as the generator.
D. Implementing spectral normalization in both the generator and discriminator.
E. Using a more powerful discriminator architecture (e.g., with attention mechanisms).

Answer: A,D

Explanation:
Spectral normalization helps stabilize training by limiting the Lipschitz constant of the discriminator and generator, preventing exploding gradients and improving image quality. A deeper and wider generator network can capture more complex image features and generate more detailed images. A simple MLP wouldn't be suitable for generating high-resolution images. Reducing the input noise vector size might limit the diversity of generated images. A more powerful discriminator helps in better distinguishing between real and fake images, which guides the generator to produce more realistic outputs. However, spectral normalization directly addresses stability issues that cause artifacts.

NEW QUESTION # 156
You are developing a virtual assistant using NVIDIAACE. You want to ensure that the avatar's facial expressions and lip movements are synchronized with the generated speech in real-time. Which NVIDIA SDKs and ACE components are essential for achieving this?

A. NeMo for text-to-speech, Audio2Face for generating blendshape weights, and a real-time rendering engine (e.g., Unity or Unreal Engine) to drive the avatar.
B. Riva for speech recognition, Triton Inference Server for model deployment, and Omniverse for 3D rendering.
C. CUDA for GPU acceleration, TensorRT for model optimization, and Audi02Emotion for expression generation.
D. Riva for speech synthesis, NeMo for language modeling, and Audi02Face for animation.
E. Triton Inference Server for deploying all AI models, Riva for voice cloning, and Omniverse for character creation.

Answer: A

Explanation:
Achieving real-time synchronized facial animation requires a text-to-speech engine (NeMo), a system to generate blendshape weights from the audio (Audi02Face), and a rendering engine to display the animated avatar. Riva provides speech recognition, not necessarily synthesis in this case. While Omniverse is useful for 3D rendering (B,D), it isn't strictly required. CUDA and TensorRT (E) are foundational but don't directly address animation.

NEW QUESTION # 157
You are tasked with building a system that can answer questions based on both an image and a corresponding text description. The image is represented as a feature vector from a CNN, and the text is represented as a sequence of word embeddings from a pre-trained language model. Which architecture would be most suitable for this task?

A. A Transformer-based architecture with cross-attention mechanisms that allow the model to attend to both the image and text features simultaneously.
B. A combination of a CNN and an LSTM, where the CNN processes the image and the LSTM processes the text independently.
C. A recurrent neural network (RNN) that processes the text and then uses the final hidden state to attend to the image features.
D. A simple feedforward neural network that concatenates the image and text feature vectors.
E. Two separate models, one for processing images and another for processing text, with the final answer being chosen based on the higher confidence score.

Answer: A

Explanation:
A Transformer-based architecture with cross-attention is best suited for this task. It allows the model to learn complex relationships between the image and text features, enabling it to answer questions that require understanding of both modalities. RNNs can be effective but might struggle with long-range dependencies. Simple feed forward networks lack the ability to capture sequential information in the text. Two seperate models cannot understand both the image and text features simultaneously. CNN and LSTM are not adequate for this type of question answering task.

NEW QUESTION # 158
You're working with a text-to-image generation model. After training, you notice the generated images lack fine-grained details and appear blurry. Which hyperparameter tuning strategy would be MOST effective in improving the visual quality of the generated images, considering the computational cost?

A. Switching to a different model architecture (e.g., from VAE to GAN).
B. Increasing the number of training epochs.
C. Optimizing the learning rate schedule.
D. Decreasing the batch size.
E. Adding more layers to the discriminator network (if using GANs).

Answer: C

Explanation:
Optimizing the learning rate schedule can have a significant impact on the quality of the generated images. A well-tuned learning rate can help the model converge to a better solution and avoid getting stuck in local minima. Increasing the number of training epochs may help, but also increases computational cost and can lead to overfitting. Adding more layers to the discriminator is a valid approach to consider if using GANs. While switching to a different architecture is an option, it would need to be justified by experimental results and may have other implications.

NEW QUESTION # 159
You have developed a multimodal model that predicts stock prices using news articles (text), historical stock data (time-series), and company financial reports (tabular data). You want to deploy this model using NVIDIA Triton Inference Server. Assume you have preprocessed the data and have individual models for each modality. What is the recommended approach to configure Triton for efficient and scalable multimodal inference?

A. Convert all models to TensorRT for maximum inference speed, even if it compromises accuracy due to quantization.
B. Create a single Triton model that encapsulates the entire multimodal pipeline, including preprocessing, individual modality models, and fusion logic, using the Ensemble Modeling feature.
C. Deploy each modality-specific model as a separate Triton model and handle the fusion logic in the client application.
D. Deploy the text model using ONNX Runtime, the time-series model using TensorFlow, and the tabular data model using PyTorch, and handle fusion manually.
E. Deploy each modality-specific model as a separate Triton model and use a load balancer to distribute requests across the models.

Answer: B

Explanation:
Using Triton's Ensemble Modeling feature (B) is the most efficient approach. It allows you to define a pipeline that includes preprocessing, individual modality models, and fusion logic within a single Triton model, simplifying deployment and management. This approach optimizes inter-model communication and reduces client-side overhead.

NEW QUESTION # 160
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