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NVIDIA Generative AI Multimodal 認定 NCA-GENM 試験問題:

1. Consider a generative AI model that combines text and audio inputs to produce a musical composition. The text input is a description of the desired mood and style, while the audio input is a short melody. Which of the following loss functions would be MOST appropriate for training this model?

A) Cross-entropy loss
B) A combination of perceptual loss (based on audio features like pitch and timbre) and a style loss (based on the text description's mood).
C) Mean Squared Error (MSE) loss
D) Wasserstein loss (Earth Mover's Distance)
E) Hinge Loss

2. You are building a multimodal emotion recognition system that uses facial expressions (images) and speech (audio). You want to use transfer learning to leverage pre-trained models for both modalities. You have access to a large pre-trained facial recognition model (trained on millions of faces) and a large pre-trained speech recognition model (trained on thousands of hours of speech). How do you design a multimodal transfer learning strategy to efficiently train the entire system on a smaller dataset of peoples face and audio samples?

A) Train the face model first, then train the audio model to recognize emotions based on the results of the facial expression emotions.
B) Use the features of the face data as an attention mechanism to pay attention to the audio, in an end-to-end learning model.
C) Fine-tune each of the pre-trained models for the emotion recognition task using a joint loss function that combines the outputs of face emotion and speech emotion to create an overall expression.
D) Train the Audio model first, then train the Face model to recognize emotions based on the results of the audio expression emotions.
E) Extract features separately using each of the pre-trained face and speech models and then train a separate classifier model, combining those features to recognize emotion.

3. You are tasked with evaluating a text-to-video generation model. Which of the following metrics would be MOST appropriate for assessing the temporal coherence and smoothness of the generated videos?

A) Frchet Video Distance (FVD)
B) Inception Score (IS)
C) BLEU score
D) Frchet Inception Distance (FID)
E) Learned Perceptual Image Patch Similarity (LPIPS)

4. Consider the following PyTorch code snippet for a GAN discriminator:

A) The code will train without errors, but the discriminator's performance will be poor due to vanishing gradients.
B) The code will train without errors, but there is no significant impact on the discriminator.
C) The code will raise a 'ValueErroN' because 'torch.mean' expects a 'dim' argument.
D) The code implements a hinge loss, encouraging the discriminator to output values greater than 1 for real samples and less than -1 for fake samples.
E) The code implements a non-saturating loss, designed to alleviate vanishing gradients in the discriminator.

5. You are developing a multimodal generative A1 model that takes both image and text inputs. The image branch uses a ResNet50 pre- trained on ImageNet, while the text branch uses a BERT model. To effectively combine the features, you need to align their representations. Which of the following techniques is MOST suitable for projecting the image and text features into a common embedding space?

A) Fine-tuning the entire ResNet50 and BERT models jointly on the multimodal dataset.
B) Using Principal Component Analysis (PCA) to reduce the dimensionality of ResNet50 and BERT features before concatenation.
C) Direct concatenation of ResNet50 and BERT output features.
D) Employing Contrastive Learning with a shared embedding space and using positive and negative pairs of image and text.
E) Training separate linear projection layers for both ResNet50 and BERT outputs, followed by concatenation.

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