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

1. You are building a system to generate captions for images. You want to evaluate how well the generated captions describe the content of the images. Which of the following metrics are most suitable for evaluating the quality of image captions?

A) Inception Score.
B) ROUGE (Recall-Oriented Understudy for Gisting Evaluation).
C) Pixel Accuracy.
D) BLEU (Bilingual Evaluation Understudy).
E) F 1-Score

2. Which of the following statements accurately describes the role of attention mechanisms in Transformer-based multimodal models?
(Select all that apply)

A) Attention mechanisms prevent vanishing gradients during training of deep neural networks.
B) Attention mechanisms are used to compress the input sequence into a fixed-length vector representation.
C) Attention mechanisms are primarily used to reduce the computational cost of processing long sequences.
D) Attention mechanisms enable the model to learn relationships between different modalities, such as images and text.
E) Attention mechanisms allow the model to focus on the most relevant parts of the input sequence when generating the output.

3. You are developing a text-to-image generative model and want to evaluate the quality and diversity of the generated images. Which metric is MOST appropriate for assessing the diversity of generated images, considering computational efficiency is also important?

A) Learned Perceptual Image Patch Similarity (LPIPS)
B) Multi-Scale Structural Similarity Index (MS-SSIM)
C) Inception Score (IS)
D) Frechet Inception Distance (FID)
E) Number of unique images in the generated set.

4. You are building a multimodal model to generate realistic dialogues between virtual characters in a game. The model takes as input the current game state (including character positions, objects, and environment), the character's personality profile (text), and the previous dialogue utterances (text and audio). What specific techniques can you employ to ensure that the generated dialogues are contextually relevant, coherent, and emotionally appropriate?

A) Train each mode separately to achieve the best result and them merge at the end.
B) Implement a hierarchical dialogue generation architecture that first plans the overall dialogue structure and then generates individual utterances.
C) Use reinforcement learning to train the model to maximize a reward function that reflects the desired dialogue characteristics (e.g., coherence, emotional appropriateness).
D) All of the above. Except D
E) Incorporate attention mechanisms that allow the model to selectively focus on the most relevant aspects of the game state and character personality profile.

5. Consider a scenario where you are training a multimodal Generative A1 model using both image and text dat a. The image data is stored in a directory with millions of high-resolution images, and the text data is in a large CSV file. What is the MOST efficient way to load and preprocess this data for training, minimizing memory usage and maximizing throughput?

A) Resize all images to a very small size before loading them into memory.
B) Load all images and text data into memory at once, preprocess them, and then feed them to the model.
C) Use a data generator (e.g., TensorFlow's 'tf.data.Dataset' or PyTorch's *DataLoadeN) to load and preprocess data in batches.
D) Convert all images to a single large video file and load it into memory along with the text data.
E) Use a distributed file system to store the data and load it directly into the model during training.

質問と回答：