NCA-GENM Question 55
Select 3You are working on a multimodal generative AI project that combines text and image data to generate product descriptions. During the data preparation phase, you notice missing values in the text dataset and inconsistent color labels in the image metadata. Which of the following steps are most appropriate for inspecting, cleansing, and transforming the data to prepare it for modeling?
- A
Use imputation techniques to fill missing text values and normalize the color labels in the image metadata.
- B
Remove all rows with missing values to ensure data consistency.
- C
Apply data augmentation techniques to the image data to address the inconsistent labels.
- D
Standardize the color labels by mapping them to a predefined set of categories.
- E
Inspect the dataset for additional anomalies using visualization tools such as histograms and scatter plots.
Show answer and explanation
Correct answers: A, D, E
Explanation
In a multimodal generative AI project, inspecting, cleansing, and transforming data is crucial for effective modeling. Imputation addresses missing values, and normalizing inconsistent metadata ensures uniformity. Standardizing categories and using visualization tools can uncover and resolve issues, improving data quality and supporting better decision-making.
- A. Correct.
Correct: Imputation techniques are commonly used to handle missing text values, and normalizing inconsistent metadata ensures the dataset is ready for analysis and modeling.
- B. Incorrect.
Incorrect: Removing all rows with missing values can lead to loss of valuable data, which is not ideal unless the missing data is pervasive and critical.
- C. Incorrect.
Incorrect: Data augmentation is used to increase dataset size and variability, not to correct metadata inconsistencies like color labels.
- D. Correct.
Correct: Standardizing color labels ensures uniformity in the metadata, which is necessary for consistent model training.
- E. Correct.
Correct: Visualizing the dataset helps identify other potential anomalies or patterns that may require preprocessing.