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

2. Consider the following code snippet used within a U-Net architecture. What is its purpose?
torch.cat ([up, skip], dim=1)

A) It performs a matrix multiplication between the 'up' and 'skip' tensors.
B) It multiplies the 'up' and 'skip' tensors element-wise.
C) It subtracts the 'skip' tensor from the 'up' tensor.
D) It concatenates the 'up' and 'skip' tensors along the channel dimension.
E) It performs an element-wise addition of the 'up' and 'skip' tensors.

3. Consider the following Python code snippet utilizing the Hugging Face Transformers library for multimodal processing. The objective is to perform visual question answering (VQA). Assume 'image' is a PIL Image object and 'question' is a string. However, the code is incomplete. Choose the options to complete the code.

A)

B)

C)

D)

E)

4. You are building a multimodal RAG application that integrates text documents and images. You've noticed that when a user query relates strongly to the visual content, the retrieved documents are less relevant than desired. Which of the following strategies would MOST effectively improve the retrieval of relevant information in this scenario?

A) Implement cross-modal embedding, training a model to create a joint embedding space for text and images.
B) Fine-tune the existing text embedding model with more text data.
C) Prepend the image captions to all the source documents to enhance text-based retrieval.
D) Use a larger language model for the generative component of the RAG pipeline.
E) Increase the k value (number of retrieved documents) in the vector search.

5. You're developing a system that translates spoken language into sign language animations. Which of the following losses would be MOST suitable for training the model to generate realistic and accurate sign language sequences from speech input?

A) Mean Squared Error (MSE) loss between the predicted joint positions of the sign language character and the ground truth joint positions.
B) Binary Cross entropy to classify the output sign animation-
C) A combination of MSE loss for joint positions and a temporal smoothness loss to encourage smooth transitions between sign language poses.
D) Cross-entropy loss between the predicted sign language sequence and the ground truth sequence.
E) Cosine Similarity loss between audio embeddings and sign language animation embeddings.

Solutions: