AWS Certified Cloud Practitioner

Explanation

Correct Answer: B - Use Bedrock pre-trained FMs and apply prompt engineering

Why Option B is correct:

1. Amazon Bedrock provides access to pre-trained foundation models (FMs) from leading AI companies

2. Prompt engineering allows customization without model training or fine-tuning

3. Minimal configuration - Bedrock offers managed inference endpoints with simple API calls

4. No model training required - Uses pre-trained models that already understand summarization tasks

Why other options are incorrect:

Option A (Amazon Comprehend):

• While Amazon Comprehend does offer text summarization, it's a specific NLP service with limited customization

• Less flexible than Bedrock for prompt engineering and using different foundation models

Option C (SageMaker training jobs):

• Requires custom datasets and model training

• Involves significant configuration, tuning, and training efforts

• Contradicts the requirement of "don't want to tune or train models"

Option D (EC2 Auto Scaling groups):

• Requires hosting and managing infrastructure

• Needs model deployment, scaling configuration, and infrastructure management

• Involves significant operational overhead and configuration

Key AWS Concepts:

• Amazon Bedrock: Fully managed service for foundation models with minimal configuration

• Prompt Engineering: Technique to guide pre-trained models without retraining

• Serverless AI: Bedrock provides serverless inference, eliminating infrastructure management

• Pre-trained Models: Ready-to-use models that don't require training or fine-tuning