- A
Amazon OpenSearch Serverless
OpenSearch Serverless is a fully managed, serverless vector store with built-in integration for Bedrock Knowledge Bases.
- B
Amazon Aurora with pgvector
Why wrong: Aurora is a provisioned database, not serverless in the context of vector search, and requires more management.
- C
MongoDB Atlas
Why wrong: MongoDB Atlas is a third-party service; while it can be used, it is not a serverless option directly integrated with Bedrock Knowledge Bases.
- D
Pinecone
Why wrong: Pinecone is a third-party vector database; it is not a native AWS service and requires separate integration.
Question 43 of 1,000
mediumMultiple ChoiceObjective-mapped
AIF-C01 Practice Question: A data scientist is designing a RAG pipeline…
This AIF-C01 practice question tests your understanding of aif-c01 exam topics. 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 data scientist is designing a RAG pipeline using Amazon Bedrock Knowledge Bases. They need to store embeddings of document chunks and perform similarity searches. Which vector store is a serverless option that integrates directly with Bedrock Knowledge Bases?
Answer choices
Why each option matters
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 natively integrates with Amazon OpenSearch Serverless as a vector store for storing embeddings and performing similarity searches. OpenSearch Serverless is a fully serverless option that automatically scales and requires no infrastructure management, making it the correct choice for a serverless vector store that works directly with Bedrock Knowledge Bases.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
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 is a fully managed, serverless vector store with built-in integration for Bedrock Knowledge Bases.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon Aurora with pgvector
Why it's wrong here
Aurora is a provisioned database, not serverless in the context of vector search, and requires more management.
- ✗
MongoDB Atlas
Why it's wrong here
MongoDB Atlas is a third-party service; while it can be used, it is not a serverless option directly integrated with Bedrock Knowledge Bases.
- ✗
Pinecone
Why it's wrong here
Pinecone is a third-party vector database; it is not a native AWS service and requires separate integration.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse 'serverless' with 'managed' and select a third-party option like Pinecone or MongoDB Atlas, which are managed but not natively integrated with Bedrock Knowledge Bases, or choose Aurora with pgvector thinking its serverless variant qualifies, but Bedrock Knowledge Bases does not support it as a direct vector store integration.
Detailed technical explanation
How to think about this question
Amazon OpenSearch Serverless uses a collection-based architecture where vector indexes are stored in distributed shards, and similarity searches leverage the k-NN (k-nearest neighbors) algorithm with support for cosine similarity, Euclidean distance, and dot product. Under the hood, it uses the Lucene engine with HNSW (Hierarchical Navigable Small World) graphs for efficient approximate nearest neighbor (ANN) search, enabling low-latency retrieval even with millions of embeddings. In a real-world RAG pipeline, this integration allows Bedrock to automatically index document chunks and retrieve relevant context without managing any servers or clusters.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Amazon OpenSearch Serverless — Amazon Bedrock Knowledge Bases natively integrates with Amazon OpenSearch Serverless as a vector store for storing embeddings and performing similarity searches. OpenSearch Serverless is a fully serverless option that automatically scales and requires no infrastructure management, making it the correct choice for a serverless vector store that works directly with Bedrock Knowledge Bases.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 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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