NVIDIA NCA-GENM

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NVIDIA Generative AI Multimodal Sample Questions:

1. You're training a multimodal model on text, image, and audio dat
a. During training, you encounter 'CUDA out of memory' errors. Your dataset is large, and you have a GPU with limited memory. Which of the following strategies would be MOST effective to mitigate this issue without significantly reducing model performance?

A) Use mixed-precision training (e.g., FP16 or BFI 6).
B) Reduce the batch size.
C) Decrease the number of layers in the model.
D) Increase the resolution of the input images.
E) Implement gradient accumulation.

2. You are tasked with evaluating the performance of a generative model that produces synthetic tabular dat a. This data will be used for downstream tasks such as training a fraud detection model. Which of the following evaluation metrics and strategies are MOST appropriate for assessing the quality and utility of the generated data in this scenario? Select all that apply.

A) Measuring the Frechet Inception Distance (FID) between the real and synthetic datasets.
B) Calculating the Jensen-Shannon Divergence (JSD) between the distributions of individual features in the real and synthetic datasets.
C) Training a fraud detection model on the synthetic data and evaluating its performance on a held-out set of real data.
D) Using Principal Component Analysis (PCA) to visualize the real and synthetic datasets in a lower-dimensional space and comparing their distributions.
E) Calculating the Structural Similarity Index (SSIM) between the real and synthetic datasets.

3. You're working with a client to develop a generative A1 model for creating personalized marketing content. During requirements acquisition, the client expresses a desire for 'highly creative' and 'unique' outputs. However, they struggle to articulate specific aesthetic preferences. How would you best approach translating these subjective requirements into concrete model training and prompt engineering strategies?

A) Implement a system for interactive prompt refinement, allowing the client to iteratively modify prompts and observe the resulting outputs in real-time, facilitating a collaborative exploration of the model's creative potential.
B) B and D
C) Focus solely on quantitative metrics like perplexity and FID score to ensure the model generates diverse and high-quality content, assuming that 'creative' and 'unique' will naturally emerge.
D) Conduct extensive A/B testing with a large user group, presenting them with various model outputs and gathering feedback on which content they perceive as most 'creative' and 'unique'. Use this feedback to refine the model and prompts.
E) Use a pre-trained style transfer model to apply different artistic styles to the generated content, offering the client a diverse range of options to choose from and identify their preferred aesthetic.

4. You're building a system to translate speech to text using an encoder-decoder architecture with attention. You observe that the translated text often repeats phrases from the input speech. Which regularization techniques could help mitigate this issue? (Select TWO)

A) Increasing the size of the vocabulary.
B) Applying label smoothing to the target sequences.
C) Decreasing the number of attention heads.
D) Adding L1 regularization to the embedding layer.
E) Adding dropout to the encoder and decoder layers.

5. You are building a multimodal generative A1 system that creates 3D models from text descriptions. The system produces accurate shapes but struggles to generate realistic textures and surface details. What approach would BEST address this limitation?

A) Train a separate texture generation network conditioned on the generated 3D shape.
B) Increase the number of parameters in the text encoder.
C) Reduce the resolution of the generated 3D models to simplify the texture generation process.
D) Add more layers to the shape decoder.
E) Increase the batch size during the 3D model generation phase.

Solutions: