NCA-GENM Question 99
Select 3You are working on a multimodal AI project that processes both text and image data. During the testing phase, you notice inconsistent results in model predictions. What steps should you take to ensure the quality and consistency of the test data in this multimodal setting?
- A
Ensure that the text and image data are properly aligned and match their respective labels.
- B
Verify that the test data distribution reflects the distribution of the training data.
- C
Focus only on the image data quality, as visual information is usually more critical in multimodal models.
- D
Check for missing or corrupted samples in both the text and image datasets.
- E
Use synthetic data to replace all real-world test samples for consistency.
Show answer and explanation
Correct answers: A, B, D
Explanation
In multimodal AI systems, ensuring the quality and consistency of test data is crucial for accurate model evaluation. Key steps include aligning multimodal data, maintaining a similar distribution between training and test datasets, and verifying the integrity of both text and image data. Neglecting any modality or solely relying on synthetic data can compromise the evaluation process.
- A. Correct.
Ensuring proper alignment between text and image data is essential in multimodal settings, as misaligned data can lead to unreliable predictions.
- B. Correct.
Testing data should reflect the distribution of training data to ensure the model generalizes well and is evaluated fairly.
- C. Incorrect.
Focusing solely on image data overlooks the importance of text data, which is equally critical in multimodal models.
- D. Correct.
Missing or corrupted samples in either modality can lead to incorrect evaluation results, so checking for such issues is vital.
- E. Incorrect.
While synthetic data can be useful for augmenting datasets, replacing all real-world test data with synthetic data can lead to inaccurate evaluation results due to lack of real-world variability.