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NVIDIA Generative AI Multimodal Sample Questions:

1. When using prompt engineering with text-to-image models, which of the following techniques are most effective in improving the fidelity and relevance of generated images to the input text?

A) Using vague and open-ended prompts to encourage creative variations.
B) Using highly specific and detailed prompts, including attributes, style, and composition.
C) Using a combination of highly specific prompts and negative prompts.
D) Using negative prompts to explicitly exclude undesirable elements from the generated image.
E) Focusing solely on the main subject of the image, omitting any contextual details.

2. You are building a system that generates image captions from images and vice vers a. Which evaluation metric(s) are MOST appropriate to assess the quality of the generated content? (Select all that apply)

A) FID (Frechet Inception Distance)
B) ROUGE score
C) BLEU score
D) Accuracy
E) Inception Score

3. You are tasked with evaluating the trustworthiness of a multimodal A1 model that predicts diagnoses based on medical images and patient history text. Which of the following evaluation metrics or techniques are MOST relevant for assessing the model's trustworthiness in this critical application?

A) Measuring inference throughput (samples per second).
B) Robustness testing by introducing adversarial perturbations to the input data.
C) Accuracy and F1-score on a held-out test set.
D) Attribution methods (e.g., Grad-CAM) to visualize which parts of the image and text the model focuses on.
E) Calibration error, measuring the alignment between predicted probabilities and actual outcomes.

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

5. You are building a real-time image captioning system using a Transformer model. You observe significant latency issues when generating captions for high-resolution images. Which optimization strategies would be most effective in reducing the latency without significantly sacrificing caption quality? (Select all that apply)

A) Decrease the batch size during inference.
B) Implement knowledge distillation, training a smaller, faster student model to mimic the behavior of the larger Transformer model.
C) Employ model quantization techniques (e.g., INT8 quantization) to reduce model size and memory bandwidth requirements.
D) Increase the number of Transformer layers to improve caption accuracy, even if it increases latency.
E) Use mixed precision training and inference, leveraging Tensor Cores on NVIDIA GPUs.

Solutions: