Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 73 discussion

BERT (Bidirectional Encoder Representations from Transformers) is a language model designed to understand context in text by considering both the left and right sides of a word. BERT-based models are well-suited for filling in missing words in sentences due to their ability to predict masked words in a given text. This makes them ideal for tasks that require filling in missing information within text data.

D. BERT-based models: BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that has been fine-tuned for various natural language processing tasks, including text completion. BERT-based models are particularly effective at predicting missing words or filling in gaps in text because they can understand context in both directions (left and right of the missing word). This makes them ideal for suggesting potential words to fill in missing text.

**Answer: D. BERT-based models** BERT (Bidirectional Encoder Representations from Transformers) uses a **masked language modeling** approach. It learns how to predict missing or “masked” words in a sentence based on the surrounding context. This makes a BERT-based model ideal for suggesting potential words to fill in missing text.