Question 1

You are collaborating with a team to deploy a multimodal AI application that analyzes large datasets and generates predictive models and visualizations. After the initial deployment, users report that the application’s performance degrades significantly when processing data from multiple sources simultaneously. The system becomes slow, and the quality of the visualizations decreases. What is the most effective approach to resolve this performance issue?

Options :

A : Implement data caching mechanisms to reduce the load on the system

B : Decrease the resolution of the visualizations to reduce processing demands

C : Reduce the number of data sources to prevent system overload

D : Manually balance the data load across the system components

Answer: A

Question 2

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 3

You are designing a generative AI system that needs to interpret and generate both textual descriptions and corresponding images. The system must integrate these diverse data types into a coherent model framework. Which of the following is the most effective approach for achieving this integration?

Options :

A : Convert all data types into a common format, such as text, before feeding them into the model.

B : Implement a multimodal transformer that processes text and image data together.

C : Use a pre-trained text model and extend it with additional layers to process images.

D : Use a separate pipeline for each data type and merge the outputs at the final stage.

Answer: B

Question 4

You are responsible for maintaining a multimodal generative AI system that processes customer service requests by analyzing both text and voice inputs. The system must categorize the requests, generate appropriate responses, and then forward them to the relevant department. However, the system is struggling with the accurate categorization of requests when the voice data contains background noise. What is the best approach to improve the system’s performance in this scenario?

Options :

A : Replace the voice recognition module with a newer, more complex one.

B : Implement noise reduction techniques on the voice input before feeding it into the model.

C : Fine-tune the model using a larger multimodal dataset with diverse noise conditions.

D : Increase the amount of text data used for training the model.

Answer: B

Question 5

As an AI developer, it's essential to stay informed about the latest advancements in language learning models. Which of the following strategies would be most effective in ensuring you are up-to-date with both academic research and industry trends?

Options :

A : Attend webinars and online courses provided by AI companies.

B : Subscribe to and regularly read AI-focused academic journals and publications.

C : Follow influencers and AI experts on social media.

D : Participate in online AI communities and forums daily.

Answer: B

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