This AIF-C01 practice question tests your understanding of applications of foundation models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A developer is building a RAG-based Q&A bot with Amazon Bedrock Knowledge Bases. They need a managed vector store for document embeddings. Which service should they use?
Amazon OpenSearch Serverless
OpenSearch Serverless with k-NN plugin provides managed vector storage.
Amazon DynamoDB
Why wrong: DynamoDB is not a vector store; it does not support similarity search.
Amazon RDS
Why wrong: RDS is relational and not optimized for vector search.
Amazon S3
Why wrong: S3 is object storage, not a vector database.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon OpenSearch Serverless
Amazon Bedrock Knowledge Bases requires a vector store to store and query document embeddings for Retrieval-Augmented Generation (RAG). Amazon OpenSearch Serverless provides a managed, scalable vector engine that supports k-NN (k-nearest neighbor) search, making it the correct choice for this use case. It integrates natively with Bedrock Knowledge Bases to handle embedding storage and similarity search without manual infrastructure management.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon OpenSearch Serverless
Why this is correct
OpenSearch Serverless with k-NN plugin provides managed vector storage.
Related concept
Read the scenario before looking for a memorised answer.
Amazon DynamoDB
Why it's wrong here
DynamoDB is not a vector store; it does not support similarity search.
Amazon RDS
Why it's wrong here
RDS is relational and not optimized for vector search.
Amazon S3
Why it's wrong here
S3 is object storage, not a vector database.
Common exam traps
The trap here is that candidates may confuse Amazon DynamoDB or Amazon RDS as viable options because they can store data, but they lack native vector search capabilities required for RAG, leading to an incorrect choice.
Trap categories for this question
Similar concept trap
DynamoDB is not a vector store; it does not support similarity search.
Detailed technical explanation
Amazon OpenSearch Serverless uses a vector engine that supports approximate nearest neighbor (ANN) search via algorithms like HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index). When integrated with Bedrock Knowledge Bases, embeddings are generated by a foundation model (e.g., Amazon Titan Embeddings) and stored in an OpenSearch index with a knn_vector field type. A real-world scenario involves querying millions of documents where the serverless vector store automatically scales shards and replicas based on workload, avoiding the operational overhead of provisioning and tuning a traditional OpenSearch cluster.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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FAQ
Applications of Foundation Models — This question tests Applications of Foundation Models — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon OpenSearch Serverless — Amazon Bedrock Knowledge Bases requires a vector store to store and query document embeddings for Retrieval-Augmented Generation (RAG). Amazon OpenSearch Serverless provides a managed, scalable vector engine that supports k-NN (k-nearest neighbor) search, making it the correct choice for this use case. It integrates natively with Bedrock Knowledge Bases to handle embedding storage and similarity search without manual infrastructure management.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.
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