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NVIDIA Generative AI Multimodal Sample Questions:

1. You're using Stable Diffusion with a custom prompt to generate images of landscapes. You notice that the generated images consistently lack detail and appear blurry, despite increasing the number of inference steps. Which of the following prompt engineering techniques, combined with appropriate parameter tuning, is MOST likely to address this issue and improve the image's sharpness and detail?

A) Using a very short and general prompt to allow the model more freedom.
B) Specifying 'oil painting' or another artistic style to mask the lack of detail.
C) Using completely unrelated keywords to encourage the model to create something unique.
D) Adding keywords like 'photorealistic', 'high resolution', '8k', 'detailed', and adjusting the 'clip_skip' parameter.
E) Decreasing the 'guidance_scale' to allow for more creative freedom.

2. 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 negative prompts to explicitly exclude unwanted features or styles.
C) Using short, concise prompts to minimize ambiguity.
D) Using random prompts to explore the model's creative capabilities.
E) Using overly verbose and descriptive prompts to maximize detail.

3. 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) Use DeepExplainer for the image component and a simple linear SHAP explainer for the text.
D) Treat the image and text as separate models and explain each independently.
E) Represent both the image and text as numerical vectors and then apply a standard SHAP explainer.

4. You are working with a multimodal model that combines text and video data for action recognition. The text data consists of descriptions of the actions, and the video data consists of sequences of frames. You want to fuse these modalities at a late fusion stage. Which of the following approaches BEST describes late fusion?

A) Training separate models for text and video data and averaging their predictions.
B) Applying attention mechanisms to weigh different parts of the text and video data before feeding them into a shared model.
C) Concatenating the raw pixel values of video frames with the word embeddings of the text descriptions.
D) Training a single model with both text and video data as input and using a shared embedding space.
E) Training separate models for text and video data and concatenating their learned feature representations before feeding them into a final classifier.

5. You have developed a multimodal generative A1 model that generates images based on textual descriptions. You want to set up an automated system to monitor the model's performance and identify potential issues like degradation in image quality or introduction of biases over time. Which of the following components are essential for such a monitoring system? (Select THREE)

A) Automated metrics calculation pipelines to continuously compute relevant metrics like FID, CLIP score, and bias detection metrics on generated images.
B) A database to store all training data used for the model.
C) Alerting mechanisms that trigger notifications when performance metrics fall below predefined thresholds or when biases are detected.
D) A mechanism to store and analyze the text prompts used to generate the images.
E) A web interface for users to manually upload images and rate the generated images.

Solutions: