NCA-GENM Question 110
Select 3A data scientist is working on a multimodal Generative AI model that integrates text, images, and audio data. During the preprocessing phase, they notice that some audio files are missing, and certain images have inconsistent resolutions. Which of the following actions should they take to ensure the quality and consistency of the multimodal dataset?
- A
Use imputation techniques to handle missing audio data.
- B
Resize images to a consistent resolution during preprocessing.
- C
Drop all samples from the dataset that have any missing modalities.
- D
Normalize all data modalities to the same scale to ensure compatibility.
- E
Replace missing data with synthetic data generated using similar samples from the dataset.
Show answer and explanation
Correct answers: A, B, E
Explanation
Ensuring the quality and consistency of multimodal datasets requires addressing missing data and inconsistencies in individual modalities. Imputation and synthetic data generation are effective for handling missing data, while resizing images ensures consistency. Dropping samples with missing modalities should be avoided to prevent excessive data loss, and normalization is not directly relevant to resolving modality-specific issues like missing audio or image resolution inconsistencies.
- A. Correct.
Correct: Imputation techniques can help fill in missing data for the audio modality, ensuring the dataset remains complete.
- B. Correct.
Correct: Resizing images to a consistent resolution is a necessary preprocessing step to ensure uniformity across the image modality.
- C. Incorrect.
Incorrect: Dropping all samples with missing modalities can lead to significant data loss, which may negatively impact the training process.
- D. Incorrect.
Incorrect: Normalizing data is useful for numerical data but does not address issues like missing audio or inconsistent image resolutions.
- E. Correct.
Correct: Generating synthetic data for missing samples is a valid approach to maintain dataset integrity, especially when the data is scarce.