Question 1

You are experimenting with different AI model architectures for a multimodal project that integrates text, image, and audio data. One model architecture shows high accuracy on the training set but fails to generalize well to the validation set. What is the most appropriate next step to evaluate and improve this model?

Options :

A : Switch to a more complex model architecture with additional layers.

B : Increase the number of epochs to further train the model.

C : Reduce the size of the training set to decrease overfitting.

D : Use cross-validation to better evaluate the model’s generalization.

Answer: D

Question 2

You are using a multimodal generative AI model that integrates both text and image inputs to generate detailed product descriptions and corresponding visuals. However, you observe that the generated images are high-quality, but the textual descriptions are vague and lack detail. What could be the primary cause of this issue?

Options :

A : The image encoder is not properly aligned with the text encoder.

B : The learning rate used during training was too high.

C : The model was not trained for enough epochs.

D : The text data used for training was not sufficiently diverse or detailed.

Answer: D

Question 3

While testing a multimodal AI model designed to generate captions for images, you observe that the generated captions are often too literal, failing to capture the nuances or context of the images. Which strategies should you consider to improve the quality of the generated captions? (Select two)

Options :

A : Incorporate a reinforcement learning approach with human feedback

B : Reduce the size of the training dataset

C : Train the model with a higher learning rate

D : Increase the diversity of the training dataset

E : Use an image feature extraction model trained on a similar domain

Answer: A,D

Question 4

You are designing a multimodal AI system for a smart city project that monitors traffic, air quality, and public safety using video feeds, sensor data, and social media analysis. The system needs to operate continuously with high accuracy while minimizing energy consumption across multiple edge devices. Which strategy should you prioritize to achieve the best balance between energy efficiency and system performance?

Options :

A : Implement model pruning to reduce the size of the neural networks used in the system.

B : Increase the frame rate of video feeds to capture more detailed data.

C : Deploy separate models for each data modality (video, sensor, social media) without integration.

D : Use high-resolution sensors to improve the accuracy of air quality measurements.

Answer: A

Question 5

You are deploying a multimodal AI system that combines text, images, and audio to assist in emergency response decision-making. The system will be used by various agencies across different countries. Which approach will most effectively ensure that the AI system provides reliable and unbiased recommendations in diverse scenarios?

Options :

A : Using a single, high-quality dataset from a leading country with advanced emergency response systems.

B : Training the model exclusively on data from regions where it will be deployed.

C : Incorporating real-time feedback from users in different regions to continuously adapt the model.

D : Implementing strict usage guidelines to limit the system’s decision-making to only certain types of emergencies.

Answer: C