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NVIDIA Generative AI Multimodal Sample Questions:

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

2. You're building a system that takes a medical image (e.g., X-ray) and a patient's medical history (text) as input, predicting the likelihood of a specific disease. You want to use SHAP (SHapley Additive exPlanations) values to explain the model's predictions. How would you adapt SHAP to handle both image and text inputs effectively?

A) Apply KernelSHAP separately to the image and text, then combine the results.
B) Use a multimodal SHAP implementation that is designed to handle both image and text features simultaneously, considering their interaction.
C) Treat the image and text as separate models and explain each independently.
D) Use DeepExplainer for the image component and a simple linear SHAP explainer for the text.
E) Represent both the image and text as numerical vectors and then apply a standard SHAP explainer.

3. You are building a generative A1 model that combines text and image inputs to generate novel images. You have access to NVIDIA NeMo and want to leverage its pre-trained models and tools. Which NeMo modules or features would be MOST beneficial for this multimodal task? (Select all that apply)

A) NeMo's TTS models for generating image descriptions.
B) NeMo's support for PyTorch Lightning for efficient training and scaling.
C) NeMo's ASR models for processing text inputs.
D) NeMo's pre-trained language models for text understanding and feature extraction.
E) NeMo's core building blocks for constructing custom neural network architectures.

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

5. Which of the following are valid techniques for dealing with overfitting in a deep learning model trained on image data?

A) Using data augmentation techniques.
B) Implementing dropout layers.
C) Increasing the complexity of the model.
D) Reducing the amount of training data
E) Adding Ll or L2 regularization.

Solutions: