NVIDIA NCA-GENM

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

1. You are building a Generative A1 application that processes images and text. The image data has missing pixel values, and the text data contains inconsistencies in abbreviations. Which data preprocessing techniques are MOST suitable to address these issues effectively?

A) Image: Mean imputation for missing pixels; Text: Standardizing abbreviations using a predefined mapping.
B) Image: Replacing missing pixels with zero; Text: Ignoring abbreviations during analysis.
C) Image: Median imputation for missing pixels; Text: Using a fuzzy matching algorithm to correct inconsistencies in abbreviations.
D) Image: KNN imputation for missing pixels; Text: Applying regular expressions to expand abbreviations.
E) Image: Deleting rows with missing pixel values; Text: Removing all abbreviations from the text data.

2. When training a multimodal generative model for image captioning, you notice the model generates grammatically correct but generic and uninformative captions. Which technique is MOST likely to improve the in formativeness and specificity of the generated captions?

A) Increase the size of the image encoder.
B) Use beam search during inference with a large beam size.
C) Decrese the size of the vocabulary.
D) Decrease the learning rate during training.
E) Employ a diverse beam search or sampling strategy during inference to encourage exploration of different caption possibilities.

3. Given the following code snippet using NVIDIA Triton Inference Server for deploying a multimodal model:

What does 'format: FORMAT NCHW' signify for the 'image_input'?

A) The image data is in a compressed JPEG format.
B) The image data is in a channel-first format (Number of Images, Channels, Height, Width).
C) The image data is normalized to a range between 0 and 1.
D) The image data is in a channel-last format (Number of Images, Height, Width, Channels).
E) The image data is represented as a NumPy array.

4. You're designing a generative A1 system to create realistic 3D models of furniture from text descriptions. Which of the following approaches would likely yield the MOST realistic and detailed results, and how can NVIDIA's tools contribute to its success?

A) None of the above
B) Training a generative adversarial network (GAN) to directly generate 3D meshes from text descriptions, using a differentiable renderer as part of the discriminator. NVIDIA's GPUs are essential for training GANs with differentiable renderers.
C) Using a simple variational autoencoder (VAE) trained on a dataset of 3D furniture models, without any text-based guidance. NVIDIA's GPUs can accelerate the VAE training process.
D) Employing a text-to-image model like Stable Diffusion to generate 2D images of the furniture from different viewpoints, and then using multi-view stereo reconstruction to create a 3D model. NVIDIA's GPUs can accelerate both the image generation and reconstruction processes.
E) Using a rule-based system to procedurally generate 3D furniture models based on keywords extracted from the text descriptions. NVIDIA's PhysX engine can be used to simulate realistic physics interactions.

5. A research team has developed a novel multimodal model that fuses text, image, and audio dat a. They want to quantitatively evaluate the model's performance in comparison to several existing state-of-the-art models. Which of the following evaluation metrics would be MOST appropriate to assess the model's ability to generate coherent and relevant text descriptions based on the combined multimodal input?

A) Perplexity.
B) Inception Score.
C) BLEU (Bilingual Evaluation Understudy) and ROIJGE (Recall-Oriented Understudy for Gisting Evaluation).
D) Structural Similarity Index Measure (SSIM).
E) Frechet Inception Distance (FID).

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