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

2. You are building a multimodal generative A1 system that creates 3D models from text descriptions. The system produces accurate shapes but struggles to generate realistic textures and surface details. What approach would BEST address this limitation?

A) Increase the number of parameters in the text encoder.
B) Add more layers to the shape decoder.
C) Reduce the resolution of the generated 3D models to simplify the texture generation process.
D) Train a separate texture generation network conditioned on the generated 3D shape.
E) Increase the batch size during the 3D model generation phase.

3. You are working on a multimodal sentiment analysis task where you have both textual reviews and corresponding product images. You want to build an attention mechanism to identify the most relevant parts of the image that contribute to the sentiment expressed in the text. Which of the following attention mechanisms is BEST suited for generating spatial attention maps highlighting these relevant regions in the image?

A) Channel attention in the image encoder (e.g., Squeeze-and-Excitation).
B) Global average pooling of image features.
C) Self-attention in the text encoder (e.g., Transformer).
D) Spatial attention in the image encoder, conditioned on the text embedding (e.g., attention over image features based on text query).
E) Temporal attention in a video encoder.

4. Consider the following Python code snippet that utilizes a pre-trained language model from the Hugging Face Transformers library:

Which of the following statements are TRUE regarding the generated output?

A) The parameter controls the number of different completion the model should return.
B) The output will always start with "The quick brown fox jumps over the lazy".
C) The output will contain a single sequence of text generated by the GPT-2 model, starting with the provided prompt.
D) The GPT-2 model is guaranteed to generate grammatically correct and factually accurate text.
E) The output will always be exactly 50 tokens long.

5. Which of the following Python code snippets correctly demonstrates how to load pre-trained word embeddings (e.g., GloVe or Word2Vec) using spaCy and then calculate the cosine similarity between two words?

A)

B)

C)

D)

E)

Solutions: