NCA-GENM Question 113
Select 2A team is developing a multimodal generative AI model that integrates text, images, and audio data. During the data preparation stage, they observe that some audio files are missing, and some image data is incomplete. Which steps should the team take to effectively address these challenges and ensure data quality for their model?
- A
Use data imputation techniques to fill in missing audio files based on patterns in the existing data.
- B
Exclude incomplete data from the dataset to ensure only high-quality data is included.
- C
Leverage multimodal data augmentation techniques to generate synthetic samples for incomplete modalities.
- D
Implement a quality assessment pipeline to detect and flag missing or incomplete data before training.
- E
Train the model without addressing missing or incomplete data, as the model can learn to compensate for it automatically.
Show answer and explanation
Correct answers: C, D
Explanation
When dealing with multimodal data, addressing missing or incomplete information is crucial to maintain the integrity and effectiveness of the dataset. Data augmentation techniques can generate synthetic samples to fill modality gaps, while a quality assessment pipeline ensures issues are identified and resolved before training. Together, these steps improve the model's ability to learn from diverse and high-quality data.
- A. Incorrect.
Imputation is commonly used for structured data like time-series or tabular data but is not effective for audio files, as audio requires domain-specific reconstruction techniques.
- B. Incorrect.
While excluding incomplete data ensures quality, it can lead to a significant loss of valuable data, which might reduce the model's performance and generalization ability.
- C. Correct.
Data augmentation techniques can be used to generate synthetic samples for incomplete modalities, which helps maintain a balanced and diverse dataset.
- D. Correct.
A quality assessment pipeline is essential to identify, flag, and address issues like missing or incomplete data, ensuring the dataset is reliable for training.
- E. Incorrect.
Relying on the model to compensate for missing or incomplete data without addressing it introduces bias and may lead to degraded performance, particularly in multimodal scenarios.