AWS Certified AI Practitioner Practice Exam

Which type of model should the company use for suggesting words to fill in missing text due to database errors?

1. Topic modeling

2. Clustering models

3. Prescriptive ML models

4. BERT-based models

The correct answer is: BERT-based models

In the context of suggesting words to fill in missing text due to database errors, utilizing BERT-based models is the most effective choice due to their design and capabilities in understanding contextual relationships in language. BERT, which stands for Bidirectional Encoder Representations from Transformers, is specifically optimized for natural language processing tasks. It uses a transformer architecture that considers the context of a word based on all of its surrounding words, rather than just the words that precede it or follow it. This bidirectional capability allows BERT to generate more accurate predictions for missing words in a text since it can understand the overall sentence structure and context. For example, if the sentence context suggests a specific meaning or category for the missing word, a BERT-based model can leverage this understanding to suggest the most appropriate word. Other models mentioned are not as suitable for this task. Topic modeling focuses on identifying abstract topics within a set of documents, clustering models group data points based on similarity without considering the sequential order of words, and prescriptive ML models are used to recommend actions based on predictive insights rather than filling in missing text. Hence, the strength of BERT in comprehending and predicting language contextually makes it the ideal choice for word suggestion tasks involving incomplete text.