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NVIDIA Generative AI Multimodal Sample Questions:

1. You're building a system to translate customer service chat logs into summaries that a human agent can quickly review The chat logs are often informal, contain slang, and have grammatical errors. Which prompt engineering technique is MOST likely to improve the quality and accuracy of the summaries generated by a large language model (LLM)?

A) Using chain-of-thought prompting to encourage the LLM to explain its reasoning process before generating the summary.
B) Using a zero-shot prompt with a simple instruction like 'Summarize this chat log.'
C) Using a negative constraint prompt, explicitly stating what the LLM should not include in the summary (e.g., 'Do not include greetings or farewells.').
D) Using a template prompt with predefined sections and keywords to guide the summarization process and ensure consistency across different chat logs.
E) Using a few-shot prompt with several examples of chat logs and their ideal summaries, explicitly demonstrating how to handle informality and errors.

2. Consider the following scenario: You are building a multimodal system for autonomous driving that uses both camera images and LiDAR data to perceive the environment. The LiDAR data is sparse and noisy, while the camera images are rich in visual details but can be affected by lighting conditions. Which of the following fusion strategies is MOST robust and effective for combining these two modalities?

A) Train separate models for each modality and combine their predictions at the end using a simple averaging scheme.
B) Fuse the raw sensor data directly by concatenating the LiDAR point cloud and the image pixels.
C) Rely solely on the camera images and discard the LiDAR data to simplify the system.
D) Use an early fusion approach where the features extracted from each modality are combined at an early stage of the processing pipeline, allowing the model to learn cross-modal interactions.
E) Rely solely on the LiDAR data and discard the camera images to avoid lighting problems.

3. Consider a scenario where you're building a multimodal model to generate image captions. You've pre-trained a large language model (LLM) on a massive text corpus and a convolutional neural network (CNN) on ImageNet. How would you effectively combine these pre- trained components for your image captioning task, considering the need to maintain high caption quality and training efficiency?

A) Freeze the LLM, train the CNN to predict text embeddings, and then decode these embeddings into captions.
B) Use a transformer-based encoder to process both image features and text embeddings before feeding them to the LLM decoder.
C) Freeze the CNN, extract image features, and train the LLM to generate captions from these features.
D) Train the CNN and LLM separately on unrelated datasets and then combine them at inference time using a simple averaging of their outputs.
E) Fine-tune both the CNN and the LLM jointly on the image captioning dataset.

4. You're training a multimodal model for generating stories from images and audio. You use a Transformer architecture. During training, you notice that the model struggles to maintain long-range dependencies in the generated stories, leading to incoherent narratives. Which of the following techniques would be MOST effective in addressing this issue within the Transformer architecture?

A) Incorporating positional encodings and increasing the attention window size.
B) Using only audio as input.
C) Using a smaller embedding dimension.
D) Removing the self-attention mechanism.
E) Reducing the number of layers in the Transformer.

5. You are building a retrieval-augmented generation (RAG) system that utilizes a knowledge graph to enhance the responses generated by a large language model. The knowledge graph contains information about entities and their relationships extracted from both text documents and image metadat a. However, you observe that the system often retrieves irrelevant or outdated information from the knowledge graph, leading to inaccurate or misleading responses. Which of the following strategies would be MOST effective in addressing this issue?

A) Use a simpler language model for the generative component of the RAG pipeline.
B) Implement a mechanism to filter and rank the retrieved information based on relevance and recency, using both semantic similarity and temporal information.
C) None of the above.
D) Increase the size of the knowledge graph.
E) Reduce the number of entities in the knowledge graph.

Solutions: