Question 1

You are analyzing the results of a multimodal AI experiment that integrates text, audio, and video data. The goal is to identify how each modality contributes to the final model accuracy. Which approach would best allow you to identify relationships and trends between the different modalities and their impact on accuracy?

Options :

A : Apply a PCA (Principal Component Analysis) to reduce the dimensionality of the data.

B : Create a pie chart showing the proportion of data from each modality.

C : Use a confusion matrix to compare the performance of each modality.

D : Perform a correlation analysis between each modality and the accuracy score.

Answer: D

Question 2

You are developing a generative AI system that needs to create text-based narratives based on a sequence of images. Which approach would best handle this multimodal task while ensuring accurate context understanding and efficient processing?

Options :

A : Use a GAN to generate synthetic images and a rule-based system to generate text.

B : Use a Vision Transformer (ViT) to encode the images and a GPT model fine-tuned on narrative generation to generate the text.

C : Use a CNN to encode the images and an RNN to generate the text.

D : Use a ResNet model to encode the images and a BERT model to generate the text.

Answer: B

Question 3

You need to deploy an end-to-end conversational AI pipeline on NVIDIA Riva that supports multiple languages. What is the most effective approach to ensure accurate ASR, NLP, and TTS performance across different languages?

Options :

A : Use a multilingual dataset for training the ASR model and apply it to NLP and TTS as well.

B : Fine-tune individual ASR, NLP, and TTS models for each language and deploy them in parallel.

C : Train a single model that can handle all languages for ASR, NLP, and TTS.

D : Deploy separate ASR models for each language but use a common NLP and TTS model.

Answer: B

Question 4

You are developing a multimodal AI model that combines video data with sensor readings to monitor and predict equipment failures in an industrial setting. The model outputs a probability score indicating the likelihood of failure. However, your stakeholders are struggling to interpret these scores. Which visualization approach would be most effective in conveying the likelihood of equipment failure and the contributing factors?

Options :

A : Develop a gauge chart to display the real-time probability of failure, combined with a tooltip feature to explain the contributing factors

B : Use a scatter plot to show the relationship between sensor readings and the probability of failure

C : Use a line chart to display the probability scores over time

D : Implement a heatmap that shows the correlation between different sensor readings and the probability of failure

Answer: A

Question 5

You are assisting with the fine-tuning of a multimodal AI model that processes images and corresponding descriptive text to perform sentiment analysis. After several epochs, the model starts to show signs of overfitting, with the training accuracy increasing while validation accuracy plateaus. The senior team member asks for your input on how to address this issue. What optimization technique should you recommend?

Options :

A : Decrease the learning rate to avoid overfitting.

B : Add more layers to the model to capture more complex patterns.

C : Increase the number of epochs to ensure the model learns from the training data.

D : Implement dropout in the fully connected layers of the model.

Answer: D

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