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NVIDIA Generative AI Multimodal Sample Questions:

1. You are developing a text-to-image generative model and want to evaluate the quality and diversity of the generated images. Which metric is MOST appropriate for assessing the diversity of generated images, considering computational efficiency is also important?

A) Inception Score (IS)
B) Multi-Scale Structural Similarity Index (MS-SSIM)
C) Frechet Inception Distance (FID)
D) Learned Perceptual Image Patch Similarity (LPIPS)
E) Number of unique images in the generated set.

2. 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from both text documents and image metadat a. However, you observe that the system often retrieves irrelevant or outdated information from the knowledge graph, leading to inaccurate or misleading responses.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) Reduce the number of entities in the knowledge graph.
B) None of the above.
C) Use a simpler language model for the generative component of the RAG pipeline.
D) Implement a mechanism to filter and rank the retrieved information based on relevance and recency, using both semantic similarity and temporal information.
E) Increase the size of the knowledge graph.

3. You are fine-tuning a pre-trained language model for a specific task. You notice that the model performs well on the training data but poorly on the validation dat a. Which of the following techniques can help mitigate this overfitting problem? (Select TWO)

A) Use dropout regularization.
B) Decrease the batch size.
C) Increase the size of the training data.
D) Increase the learning rate.
E) Apply weight decay (L2 regularization).

4. You are working on a project that involves analyzing customer reviews which contains the following dataset: 1. customer_id(categorical) 2. customer_review(text) 3. product_image(image) 4. video_of_product_usage(video) What is the best way to handle and address the problem of skewness across each modailities?

A) Treat all modalitites with equal weights during model training, ignoring potential skewness issues.
B) Do nothing about the skewness, as the model will learn to adapt to the imbalanced data distribution.
C) Design a loss function that explicitly penalizes the model for being biased towards dominant modalities.
D) Apply modality-specific weighting schemes that assign higher weights to modalities with less representation.
E) Balance the dataset by oversampling under-represented data points within each modality independently.

5. You are deploying a multimodal Generative A1 model on a cloud platform. The model takes video and text as input to generate video descriptions. The model's performance needs to be monitored to ensure it meets certain performance SLAs. Which of the following metrics are MOST crucial to monitor in a production environment to ensure both computational efficiency and output quality? (Select TWO)

A) BLEU score (or similar text generation metric) for generated descriptions.
B) Model size on disk.
C) Inference latency (time per request).
D) Number of lines of code in the model.
E) GPU utilization.

Solutions: