최신 NCA-GENM 무료덤프 - NVIDIA Generative AI Multimodal

문제1

You are building a multimodal application that takes an image and a short text description as input and generates a more detailed text description of the image. Which of the following model architectures is BEST suited for this task?

A. A Vision Transformer (ViT) for image encoding and a Transformer for text decoding.

B. A Multilayer Perceptron (MLP) for both image and text.

C. A simple CNN followed by an LSTM.

D. A Generative Adversarial Network (GAN) with separate image and text encoders.

E. A Recurrent Neural Network (RNN) with attention mechanisms.

정답: A

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문제2

You are using the Stable Diffusion model for image generation. You want to generate an image of a 'cat wearing a hat in a cyberpunk city', but you are not satisfied with the initial results. Which of the following techniques could you use to refine the generated image and get closer to your desired outcome?

A. Decrease the CFG (Classifier-Free Guidance) scale.

B. Change the random seed to explore different variations.

C. Reduce the number of inference steps.

D. Increase the number of inference steps.

E. Use a negative prompt to exclude unwanted elements or styles.

정답: B,D,E

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문제3

You are building a multimodal generative A1 model that creates realistic indoor scenes by combining textual descriptions, floor plans (geospatial data), and object libraries. The goal is to generate high-quality 3D models of the scenes. However, the model often produces scenes with physically implausible object arrangements (e.g., objects floating in the air, overlapping furniture). How can you MOST effectively integrate physical constraints into the generation process to ensure more realistic scene compositions?

A. Force the model to generate only scenes that exist within the training set.

B. Train a separate discriminator network that evaluates the physical plausibility of generated scenes and penalizes implausible configurations during training.

C. Use a physics engine (e.g., NVIDIA PhysX) as a post-processing step to simulate the generated scene and correct any physically implausible object placements.

D. Implement a rule-based system that enforces basic physical constraints (e.g., objects must be supported by a surface, no object interpenetration) during the generation process.

E. Increase the size of the training dataset with more examples of realistic indoor scenes.

정답: B,C,D

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문제4

You are working with a dataset of handwritten digits and training a Variational Autoencoder (VAE) to generate new digits. After training, you observe that the generated digits are blurry and lack sharp details. Which of the following modifications could potentially improve the quality of the generated digits in your VAE?

A. Using a simpler decoder architecture.

B. Reducing the weight of the KL divergence term in the VAE loss function.

C. Increasing the capacity of the encoder and decoder networks (e.g., adding more layers or neurons).

D. Decreasing the dimensionality of the latent space.

E. Increasing the weight of the KL divergence term in the VAE loss function.

정답: B,C

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문제5

You are working on a project that involves training a large language model (LLM) on a massive dataset of text and code. You have limited GPU memory and need to optimize the training process. Which of the following techniques would be MOST effective in reducing memory consumption during training?

A. Gradient accumulation and mixed-precision training (e.g., using FP16 or BFIoat16).

B. Using a smaller learning rate.

C. Increasing the number of layers in the LLM.

D. Using a higher precision data type (e.g., float64 instead of float32).

E. Increasing the batch size.

정답: A

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문제6

Consider a scenario where you're building a multimodal model to generate image captions. You've pre-trained a large language model (LLM) on a massive text corpus and a convolutional neural network (CNN) on ImageNet. How would you effectively combine these pre- trained components for your image captioning task, considering the need to maintain high caption quality and training efficiency?

A. Freeze the CNN, extract image features, and train the LLM to generate captions from these features.

B. Freeze the LLM, train the CNN to predict text embeddings, and then decode these embeddings into captions.

C. Fine-tune both the CNN and the LLM jointly on the image captioning dataset.

D. Use a transformer-based encoder to process both image features and text embeddings before feeding them to the LLM decoder.

E. Train the CNN and LLM separately on unrelated datasets and then combine them at inference time using a simple averaging of their outputs.

정답: C,D

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문제7

You're developing a multimodal A1 system that takes image data, text descriptions, and user interaction data (clicks, dwell time) to generate personalized product recommendations. To effectively combine these modalities and capture complex relationships, which model architecture would be most suitable?

A. A deep learning architecture incorporating attention mechanisms and cross-modal fusion layers, with separate embedding layers for each modality, followed by a shared representation layer for joint learning and prediction.

B. A Naive Bayes classifier.

C. A k-nearest neighbors (KNN) algorithm.

D. A simple linear regression model.

E. A decision tree-based model.

정답: A

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문제8

You are building a system that uses audio and video to detect emotional states of a user. What are the challenges to this system?

A. Differences in lighting conditions influencing facial expression recognition.

B. Synchronization issues between audio and video streams.

C. Variations in background noise affecting audio quality.

D. Subjectivity in emotional expression across cultures and individuals.

E. All of the above.

정답: E

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문제9

You are working with a multimodal model that combines text and image inputs. You want to analyze the model's attention mechanisms to understand which parts of the image are most relevant to specific words in the input text. What technique can you use to visualize and interpret the model's attention weights in this scenario?

A. Confusion Matrix

B. PCA (Principal Component Analysis)

C. t-SNE (t-distributed Stochastic Neighbor Embedding)

D. Attention Heatmaps

E. ROC curves (Receiver Operating Characteristic curves)

정답: D

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문제10

Consider this PyTorch code snippet related to processing multimodal dat a. What is the primary purpose of the following code in the context of Generative A1?

A. To concatenate image and text data into a single tensor.

B. To resize all images to the same dimension.

C. To create separate data loaders for images and text.

D. To create a custom dataset class for handling paired image and text data.

E. To ensure images and text are processed in the same order during training.

정답: D

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문제11

You're developing an Avatar Cloud Engine (ACE) application to create a real-time, interactive virtual assistant. The assistant needs to respond to user speech, understand their intent, and generate appropriate responses. Which sequence of NVIDIA SDKs would provide the MOST complete solution for this task?

A. CUDA (For running deep learning workloads)-> Riva (for speech recognition and synthesis) -> ACE (for avatar rendering and animation).

B. NeMo (for training a custom language model) -> Triton Inference Server (for serving the trained language model) -> ACE (for avatar rendering and animation).

C. Riva (for speech recognition and synthesis) -> Triton Inference Server (for serving a pre-trained chatbot model) -> ACE (for avatar rendering and animation).

D. Triton Inference Server (for serving all models) -> Riva (for speech recognition and synthesis) ACE (for avatar rendering and animation).

E. Riva (for speech recognition and synthesis) -> NeMo (for natural language understanding and response generation) -> Triton Inference Server (for model deployment) ACE (for avatar rendering and animation).

정답: E

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문제12

You are experimenting with different multimodal transformer architectures for a video understanding task. You are using a large pre- trained model and fine-tuning it on your specific dataset. You observe that the model is overfitting and struggling to generalize to unseen videos. Which of the following techniques would be most effective in mitigating overfitting in this scenario? (Choose two)

A. Reduce the number of transformer layers in the model.

B. Use a smaller pre-trained model.

C. Implement weight decay and dropout regularization.

D. Employ data augmentation techniques specifically designed for video data (e.g., temporal jittering, random cropping).

E. Increase the batch size significantly.

정답: C,D

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문제13

Which of the following statements are TRUE regarding the challenges of training multimodal machine learning models? (Select TWO)

A. All available open-source tools readily support multimodal architectures and loss functions, so there are no software-related challenges.

B. Multimodal models are generally easier to train than unimodal models due to the increased information available.

C. Aligning data from different modalities with varying temporal resolutions (e.g., high-frame-rate video and low-frequency audio) is a significant challenge.

D. Multimodal models are immune to the problem of overfitting due to the diverse nature of the input data.

E. Handling missing modality data (e.g., missing image for a text input) requires specialized techniques.

정답: C,E

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최신 NCA-GENM 무료덤프 - NVIDIA Generative AI Multimodal

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