How to Supplement Amazon Titan FM with Private Data in Amazon Bedrock?

AIF-C01 By islever Editorial Last updated Aug 17, 2026

Short answer

C Create an Amazon Bedrock knowledge base.

Question

A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources. Which solution will meet this requirement?

Use a different FM.
Choose a lower temperature value.
Create an Amazon Bedrock knowledge base. Correct Answer
Enable model invocation logging.

Key Takeaways

The exam tests whether you know that Amazon Bedrock knowledge bases provide RAG to enhance FMs with private data sources, and the trap is confusing fine-tuning or inference parameters with knowledge base creation.

To supplement an Amazon Titan foundation model with private data in Amazon Bedrock, the correct solution is to create an Amazon Bedrock knowledge base, which enables Retrieval Augmented Generation (RAG) without retraining the model.

Common mistake: Choosing a lower temperature value (option B) is the most likely incorrect choice because temperature controls randomness, not knowledge supplementation, while other options like using a different FM or logging do not inject private data.

Expert Analysis

Why the Answer Is Correct

Amazon Bedrock knowledge bases are purpose-built for Retrieval Augmented Generation (RAG). They allow you to connect a foundation model like Amazon Titan to your private data sources (e.g., S3, databases, or enterprise repositories) without retraining or fine-tuning. As commenters noted, this enables the FM to query relevant documents during inference, producing more accurate and context-aware responses. The knowledge base handles ingestion, chunking, embedding, and retrieval, making it the only option that directly addresses supplementing the model with private data.

Why the Other Options Are Wrong

Option A (use a different FM) does not incorporate private data; it merely swaps the base model. Option B (choose a lower temperature value) adjusts the randomness of the model's responses but does not add external knowledge. Option D (enable model invocation logging) records API calls for monitoring/auditing and has no effect on the model's data context. Only option C matches the requirement of supplementing the FM with company-specific data.

Community Comment Notes

Multiple comments (e.g., from users who voted C) explicitly mention that the knowledge base enables RAG to enhance the model with private data sources. One commenter emphasized that this avoids the need for training or adjusting the model. Another highlighted that the knowledge base allows the FM to access specific proprietary data during inference, making responses more tailored. The unanimity of the votes (100% C) confirms that the community recognizes this as a core Bedrock feature.

Official Reference

These links open the official vendor documentation in a new tab.

Exam Strategy

Remember that whenever a question asks about using private or proprietary data with a foundation model in Bedrock, immediately think of 'knowledge base' and RAG. Do not confuse it with model fine-tuning, prompt parameters, or logging. Review the key components of Bedrock knowledge bases: data sources, ingestion pipeline, vector store, and retrieval API.

Community Votes

100%

100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 6 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 6

minime 👍 4

C. Create an Amazon Bedrock knowledge base. This would allow the company use the knowledge base for Retrieval Augmented Generation (RAG) to enhance the model's knowledge with company's private data sources.
Rcosmos 👍 1 Answered C

Explicação: O Amazon Bedrock permite que você complemente um modelo de fundação (Foundation Model – FM), como o Amazon Titan, com dados proprietários da sua empresa sem precisar treinar ou ajustar o modelo. Isso é feito por meio da criação de uma base de conhecimento, que permite que o modelo: Busque e consulte dados relevantes em fontes privadas (como documentos, bancos de dados ou repositórios internos). Ofereça respostas mais precisas e contextualizadas com base nas informações da empresa.Use a técnica conhecida como RAG (Retrieval-Augmented Generation).
Jessiii 👍 1 Answered C

Create an Amazon Bedrock knowledge base: This solution allows the company to supplement the Amazon Titan foundation model with their own relevant, private data sources. The knowledge base enables the foundation model to access specific, proprietary data during inference, enhancing the model's responses and making it more tailored to the company’s needs. This is especially useful when the model needs to be supplemented with data that is not part of the model’s initial training data.
eesa 👍 1 Answered C

Create an Amazon Bedrock knowledge base. An Amazon Bedrock knowledge base allows you to incorporate your company's proprietary data into the foundation model. By feeding the model with relevant information, you can enhance its ability to generate more accurate and informative responses.
85b5b55 👍 1 Answered C

Amazon Bedrock KB support to interact with the company's private data sources.

Show 1 more comment

raat 👍 1 Answered C

C, is correct

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