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NVIDIA Generative AI Multimodal Sample Questions:

1. You are developing a system to generate captions for videos. The video frames are processed using a pre-trained ResNet model, and the audio track is processed using a pre-trained Wav2Vec model. Which of the following techniques is MOST suitable for aligning the visual and audio features to generate accurate and coherent captions?

A) Using cross-attention mechanisms where the audio features attend to the visual features, and vice-versa, before feeding them into a Transformer decoder.
B) Concatenating the ResNet and Wav2Vec features and feeding them into a single LSTM.
C) Using a simple feedforward network to combine the ResNet and Wav2Vec features.
D) Training separate LSTMs for visual and audio features and averaging their outputs.
E) Ignoring the audio track and only using the video frames.

2. Which of the following loss functions is MOST suitable for training a multimodal model for cross-modal retrieval, where the goal is to retrieve relevant images given a text query and vice versa?

A) Triplet loss.
B) Cross-entropy loss.
C) KL Divergence.
D) Binary Cross-entropy loss.
E) Mean Squared Error (MSE) loss.

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

4. You're building a system that uses a pre-trained large language model (LLM) for generating creative stories. After deploying the system, you notice that the generated stories often contain biases present in the training data of the LLM. What are the MOST effective strategies to mitigate these biases in your generated stories? (Select TWO)

A) Fine-tune the LLM on a diverse and representative dataset.
B) Use prompt engineering to steer the LLM away from biased outputs.
C) Reduce the size of the LLM to minimize memory usage.
D) Apply bias detection and mitigation techniques to the LLM's output.
E) Increase the temperature parameter in the LLM's decoding strategy.

5. You are building a multimodal Generative AI model that takes text and images as input to generate a story. The text encoder uses a pre-trained BERT model, and the image encoder uses a pre-trained ResNet50 model. What is the BEST strategy to align the feature spaces of these two encoders during training to ensure effective multimodal fusion?

A) Concatenate the outputs of BERT and ResNet50 directly without any alignment strategy.
B) Fine-tune only the ResNet50 model while keeping the BERT model frozen.
C) Train a separate linear projection layer for each encoder and minimize the LI distance between the projected features. Freeze BERT and ResNet50.
D) Fine-tune only the BERT model while keeping the ResNet50 model frozen.
E) Use a contrastive loss function that encourages similar representations for semantically related text and images, and dissimilar representations otherwise. Fine-tune BERT and ResNet50.

Solutions: