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NVIDIA Generative AI Multimodal Sample Questions:

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

2. You are building a multimodal model for medical diagnosis that combines patient medical history (text), medical images (X-rays, MRIs), and sensor data (heart rate, blood pressure). The dataset contains significant amounts of missing data across all modalities. What strategy is most appropriate for handling the missing data and ensuring the model's robustness and accuracy?

A) Imputing missing values using simple methods like mean imputation or filling with a constant value.
B) Using a multimodal variational autoencoder (MVAE) to learn a joint latent representation of the data and impute missing values based on the observed modalities.
C) Training seperate models for each avalible modality.
D) Using a Generative Adversarial Network(GAN) to impute missing values based on the other avalible modalities.
E) Removing all patients with missing data to create a clean dataset.

3. 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 zero-shot prompt with a simple instruction like 'Summarize this chat log.'
B) Using a template prompt with predefined sections and keywords to guide the summarization process and ensure consistency across different chat logs.
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 few-shot prompt with several examples of chat logs and their ideal summaries, explicitly demonstrating how to handle informality and errors.
E) Using chain-of-thought prompting to encourage the LLM to explain its reasoning process before generating the summary.

4. Consider a multimodal dataset consisting of product reviews (text), product images, and customer demographics. You want to build a model that can predict customer satisfaction based on all three modalities. However, you suspect that there might be complex interactions between these modalities that are not easily captured by simple concatenation or averaging. What approach would be most effective for modeling these interactions?

A) Using a tensor fusion network that explicitly models higher-order interactions between modalities.
B) Concatenating the feature vectors from each modality and feeding them into a single fully connected layer.
C) Using a gated recurrent unit (GRU) to process the combined data.
D) Employing transfer learning, using pre-trained models for image and text processing then using the final combined layer for downstream task.
E) Training separate models for each modality and then averaging their predictions.

5. You are developing a multimodal system for generating recipes from images of food. The system takes an image of a dish as input and outputs a recipe containing the ingredients and instructions. Which of the following evaluation metrics would be most suitable for assessing the correctness and completeness of the generated recipes? (Select all that apply)

A) Calculating the cosine similarity between the word embeddings of the generated and reference recipes.
B) Precision and recall of the ingredients mentioned in the generated recipe compared to a ground truth ingredient list.
C) Inception Score of the input image.
D) BLEU score between the generated recipe and a reference recipe.
E) Human evaluation of the generated recipe's clarity, coherence, and accuracy.

Solutions: