Question 1

Your company is developing a multimodal AI application that combines text, image, and audio inputs. As part of the rapid development process, you are tasked with integrating a text-to-image model from HuggingFace and experimenting with it to see if it meets your needs. How can you most efficiently pull in and start experimenting with a text-to-image model from HuggingFace using the Transformers API?

Options :

A : Utilize the transformers.Pipeline with the text-to-image task and load the model directly from the HuggingFace repository.

B : Download the model-s code and modify it to suit the text-to-image task within your application.

C : Use the AutoModelForVision class to load the text-to-image model and begin testing.

D : Use the AutoModelForImageAndText class to load the model and start experimenting.

Answer: A

Question 2

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 3

You are tasked with improving a multimodal neural network used for predicting patient outcomes based on medical imaging, lab results, and clinical notes. The current model struggles with learning complex features due to the depth of the network. Which benefit of residual connections would most directly address this problem?

Options :

A : Residual connections enable the network to learn complex features without degradation of performance

B : Residual connections allow for sequential processing of each modality independently

C : Residual connections improve the interpretability of the model’s outputs

D : Residual connections reduce the number of parameters in the network

Answer: A

Question 4

You need to customize a TTS model using NVIDIA Riva to generate speech that conveys different emotions, such as happiness, sadness, and anger. Which strategy is most effective for this task?

Options :

A : Incorporate more training data without focusing on emotional content.

B : Use transfer learning from an ASR model trained on emotional speech.

C : Use a standard TTS model and manually adjust the pitch and speed for different emotions.

D : Fine-tune the TTS model on a dataset labeled with different emotional states.

Answer: D

Question 5

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