AWS Certified Cloud Practitioner

Explanation

BERT-based models are specifically designed for natural language processing tasks involving text understanding and generation. For the task of suggesting potential words to fill in missing text, BERT (Bidirectional Encoder Representations from Transformers) is particularly well-suited because:

1. Masked Language Modeling: BERT is trained using a masked language modeling objective where it learns to predict missing words in sentences, making it ideal for this exact use case.

2. Contextual Understanding: BERT understands the context from both directions (left and right of the missing word), allowing it to make accurate predictions based on surrounding text.

3. Pre-trained Capabilities: BERT models come pre-trained on large text corpora and can be fine-tuned for specific domains, making them efficient for text completion tasks.

Why other options are incorrect:

• A. Topic modeling: This is used to discover abstract topics within documents, not for word prediction.
• B. Clustering models: These group similar data points together, not for predicting missing words.
• C. Prescriptive ML models: These recommend actions or decisions based on predictions, not specifically for text completion.

BERT-based models are the standard choice for natural language understanding tasks including text completion, question answering, and sentiment analysis.