NCA-GENM Question 62
Select 3A team is building a generative AI model that processes multimodal data, including text, images, and sensor data. They want to extract meaningful insights from this data before training the model. Which combination of techniques would best help them identify patterns, visualize relationships, and prepare the data effectively?
- A
Data mining to uncover hidden patterns in the dataset
- B
Data visualization to identify trends and correlations between variables
- C
Random sampling to reduce the dataset size without analyzing it further
- D
Using pre-trained models to skip the data preparation step
- E
Dimensionality reduction to simplify large datasets while preserving key information
Show answer and explanation
Correct answers: A, B, E
Explanation
To extract meaningful insights from multimodal data before training a generative AI model, a combination of data mining, data visualization, and dimensionality reduction is essential. These techniques help uncover hidden patterns, visualize relationships, and reduce dataset complexity while preserving important information. Random sampling and skipping data preparation with pre-trained models are not effective for extracting insights.
- A. Correct.
Data mining involves analyzing large datasets to uncover hidden patterns, making it essential for understanding complex multimodal data.
- B. Correct.
Data visualization helps in visually exploring relationships and trends in the data, which is critical for identifying insights before training.
- C. Incorrect.
Random sampling may reduce dataset size but does not directly help in extracting meaningful insights or understanding relationships in the data.
- D. Incorrect.
Using pre-trained models skips the data preparation and analysis step, which is not recommended if insights need to be extracted and understood.
- E. Correct.
Dimensionality reduction is valuable for simplifying datasets while retaining important features, making it a useful step in preparing large multimodal datasets.