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NVIDIA Generative AI Multimodal Sample Questions:

1. You have a multimodal model that takes video and audio as input for activity recognition. You want to evaluate the impact of different fusion strategies (early fusion, late fusion, intermediate fusion) on the model's accuracy and computational cost. Which of the following statements is generally TRUE regarding these fusion strategies?

A) Intermediate fusion is always superior to both early and late fusion in terms of accuracy.
B) Early fusion is always the best choice for real-time applications due to its low latency.
C) Late fusion generally easier to implement than early fusion as it doesn't require modification to the individual modality encoders.
D) Late fusion typically has the highest computational cost but allows for the most effective interaction between modalities.
E) Early fusion typically has the lowest computational cost but may limit the model's ability to capture modality-specific features.

2. You're building a system that generates images from text descriptions, incorporating spatial relationships. For instance, the text 'A red ball is to the left of a blue cube' should result in an image where the red ball is actually positioned to the left of the blue cube. Which of the following approaches would be MOST suitable for encoding and utilizing spatial information in this text-to-image generation process?

A) Relying solely on the image decoder to learn spatial relationships implicitly from the text description during training.
B) Augmenting the text encoder with explicit spatial relation embeddings that represent the relative positions between objects. Use these embeddings to modulate the image generation process (e.g., through attention mechanisms).
C) Applying a pre-trained object detector to the generated image and penalizing the model if the spatial relationships are incorrect
D) Using a standard Transformer architecture for text encoding without any specific spatial awareness mechanisms
E) Using a bag-of-words representation for the text, ignoring word order and spatial relationships.

3. You are tasked with evaluating the performance of a generative model that produces synthetic tabular dat a. This data will be used for downstream tasks such as training a fraud detection model. Which of the following evaluation metrics and strategies are MOST appropriate for assessing the quality and utility of the generated data in this scenario? Select all that apply.

A) Calculating the Structural Similarity Index (SSIM) between the real and synthetic datasets.
B) Calculating the Jensen-Shannon Divergence (JSD) between the distributions of individual features in the real and synthetic datasets.
C) Using Principal Component Analysis (PCA) to visualize the real and synthetic datasets in a lower-dimensional space and comparing their distributions.
D) Measuring the Frechet Inception Distance (FID) between the real and synthetic datasets.
E) Training a fraud detection model on the synthetic data and evaluating its performance on a held-out set of real data.

4. You have a text-to-image model deployed using Triton Inference Server. You want to monitor the GPU utilization and inference latency to ensure optimal performance. Which of the following methods is the MOST effective way to achieve this?

A) Writing custom scripts to parse Triton's log files and extract performance metrics.
B) Using the Triton Inference Server client API to measure inference latency from the client-side.
C) Using Triton's built-in Prometheus metrics endpoint and Grafana for visualization.
D) Relying solely on the operating system's resource monitor to track GPU usage.
E) Using 'nvidia-smi' to periodically check GPU utilization and manually calculate latency.

5. You are working with a large dataset of images to train a Generative A1 model. You suspect that some images are corrupted or of poor quality, which could negatively impact training. Which of the following methods would be the MOST effective in identifying and removing these problematic images?

A) Calculate the average pixel intensity for each image and remove those with very low or very high average intensity.
B) Compute the image sharpness (e.g., using Laplacian variance) and remove images with low sharpness values.
C) Manually inspect each image and remove those that appear to be corrupted or low quality.
D) Use a pre-trained image quality assessment model (e.g., BRISQUE, NIQE) to score each image and remove those with low scores.
E) Check for file corruption errors during image loading and remove those files.

Solutions: