NVIDIA Generative AI Multimodal : NCA-GENM

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NVIDIA Generative AI Multimodal Sample Questions:

1. You are tasked with integrating a CLIP model into your application to generate images based on text descriptions. You want to ensure that the generated images closely reflect the nuances of the text prompt. Which prompt engineering technique is MOST suitable for achieving this?

A) Using prompts consisting only of keywords related to the desired image.
B) Using overly verbose and descriptive prompts to maximize detail.
C) Using negative prompts to explicitly exclude unwanted features or styles.
D) Using random prompts to explore the model's creative capabilities.
E) Using short, concise prompts to minimize ambiguity.

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

3. You are working on a project that involves analyzing customer reviews which contains the following dataset: 1. customer_id(categorical) 2. customer_review(text) 3. product_image(image) 4. video_of_product_usage(video) What is the best way to handle and address the problem of skewness across each modailities?

A) Apply modality-specific weighting schemes that assign higher weights to modalities with less representation.
B) Balance the dataset by oversampling under-represented data points within each modality independently.
C) Design a loss function that explicitly penalizes the model for being biased towards dominant modalities.
D) Do nothing about the skewness, as the model will learn to adapt to the imbalanced data distribution.
E) Treat all modalitites with equal weights during model training, ignoring potential skewness issues.

4. You are building a multimodal emotion recognition system that takes both facial expressions (images) and speech audio as input. During development, you observe that the model is heavily biased towards the audio modality, effectively ignoring the visual input. Which technique would be the LEAST effective in mitigating this modality bias?

A) Increasing the complexity of the audio processing branch and simplifying the image processing branch of the model.
B) Modality dropout: Randomly dropping out one of the modalities during training.
C) Gradient blending: Adjusting the gradients from each modality based on their relative importance.
D) Reweighting the loss function to penalize errors made based on the less dominant modality (image).
E) Adversarial training to make each modality indistinguishable.

5. You're working on a multimodal AI system that combines text and image dat a. You're using a contrastive learning approach to learn joint embeddings of text and images. However, you notice that the system performs well on seen image-text pairs but poorly on unseen combinations. What technique MOST directly addresses this generalization problem?

A) Increasing the embedding dimension-
B) Implementing hard negative mining.
C) Using a larger batch size during training.
D) Using a simpler model architecture-
E) Decreasing the temperature parameter in the contrastive loss.

Solutions: