NCA-GENL Question 115
Select 3A retail company plans to use Generative AI to better understand customer preferences and improve product recommendations. Before implementing the model, the team needs to extract insights from their large customer dataset, which includes purchase history, browsing behavior, and customer reviews. Which of the following techniques are key steps in processing this data to extract actionable insights?
- A
Data cleaning to handle missing or inconsistent data in the dataset
- B
Data visualization to identify patterns and trends in customer behavior
- C
Directly training a Generative AI model on raw data without preprocessing
- D
Using clustering to segment customers based on similar purchasing habits
- E
Ignoring text data like customer reviews as it is unstructured
Show answer and explanation
Correct answers: A, B, D
Explanation
Extracting insights from large datasets involves multiple steps, including cleaning the data to ensure quality, visualizing it to uncover trends, and applying techniques like clustering to group similar entities. These steps provide a solid foundation for training AI models. Ignoring preprocessing or valuable unstructured data can lead to incomplete or inaccurate insights, reducing the effectiveness of any Generative AI solution.
- A. Correct.
Data cleaning is an essential step to ensure the dataset is accurate, consistent, and free from errors, which is critical for meaningful analysis and model training.
- B. Correct.
Data visualization helps identify trends, patterns, and anomalies in the data, which can guide decision-making and model design.
- C. Incorrect.
Training a Generative AI model on raw data without preprocessing is not a recommended approach, as unprocessed data can introduce noise and errors into the model, reducing its performance.
- D. Correct.
Clustering is a powerful data mining technique to group customers with similar behaviors, enabling more targeted insights and recommendations.
- E. Incorrect.
Ignoring text data like customer reviews overlooks valuable information that can provide deeper insights into customer preferences and sentiments.