Databricks Certified Generative AI Engineer - Associate

Explanation

Text Classification Model focused on Sentiment Analysis (B):

This type of model is specifically designed to determine the overall sentiment of a text (positive, negative, or neutral). It is highly effective for classifying customer reviews at a high level, providing a clear understanding of the sentiment expressed in each review.

Text Classification Model with Aspect-Based Sentiment Analysis (ABSA) (C):

ABSA extends sentiment analysis by identifying sentiments associated with specific aspects or features mentioned in the text. For example, it can analyze mentions of product features (e.g., "battery life," "design") and determine whether the sentiment about those features is positive, negative, or neutral. This capability makes ABSA ideal for understanding the reasons behind the sentiment, aligning perfectly with the e-commerce company's requirements.

Why not the others:

• A. Named Entity Recognition (NER) Model:
  While NER is effective for identifying specific entities (e.g., product names, brands), it does not perform sentiment analysis or extract the reasons for sentiment in text.

• D. Sequence-to-Sequence (Seq2Seq) Model for Text Generation:
  Seq2Seq models are typically used for tasks like language translation, text summarization, or text generation. They are not optimized for sentiment analysis or feature-based sentiment extraction.

• E. Topic Modeling for Latent Semantic Analysis:
  Topic modeling identifies overarching themes or topics in text but does not evaluate sentiment or reasons for sentiment. It is less precise for this use case compared to sentiment-specific models.

B and C together address both aspects of the problem: determining overall sentiment and identifying the reasons behind it, making them the most effective choices.