Practice Tests | AWS Certified Solutions Architect Associate

Based on the scenario provided, the most appropriate solutions utilizing AWS services are:

1. Set up Amazon Kendra index to ingest and process documents from multiple sources. Amazon Kendra is an intelligent search service powered by machine learning that allows you to create an index of your content and enables users to query and retrieve information using natural language. It supports question-answering style queries and provides built-in machine learning models to understand the intent behind complex search queries.
2. Configure Kendra’s natural language processing capabilities for multiple languages. Amazon Kendra already supports multiple languages out of the box, which means you can enable multilingual support without significant custom development or additional integrations. This will allow users to perform searches in their preferred language and understand content across different languages.

The other options are less suitable for the requirements specified:

• Implement a traditional keyword-based search engine using Elasticsearch. While Elasticsearch is powerful and flexible, it does not inherently provide the advanced natural language understanding (NLP) and question answering capabilities that Kendra offers.
• Use Amazon Comprehend for text analysis and integrate with a custom search solution. This approach would require significant custom development to create a search solution from scratch, which may not offer the out-of-the-box NLP features and multilingual support that Kendra provides.
• Deploy a full-text search solution using Amazon RDS and custom indexing. Amazon RDS is a managed database service and is not designed for full-text search use cases. It would be more complex, less efficient, and would require custom development compared to using Kendra.

In summary, Amazon Kendra provides a robust and scalable solution that meets the needs of the company’s knowledge base search requirements with multi-language support out of the box.