- A
Pinecone
Why wrong: Pinecone is a third-party vector database, not an AWS managed service.
- B
Amazon Aurora with pgvector extension
Amazon Aurora supports pgvector for vector storage and search, and is fully managed.
- C
MongoDB Atlas
Why wrong: MongoDB Atlas is a third-party service, not an AWS managed service.
- D
Amazon DynamoDB
Why wrong: DynamoDB is a key-value and document database, but does not natively support vector search for production RAG (no pgvector equivalent).
- E
Amazon OpenSearch Serverless
Amazon OpenSearch Serverless provides a managed vector engine for low-latency similarity search.
Question 463 of 1,000
mediumMultiple SelectObjective-mapped
AIF-C01 Practice Question: Building a generative AI application using Amazon…
This AIF-C01 practice question tests your understanding of aif-c01 exam topics. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 company is building a generative AI application using Amazon Bedrock that requires low-latency responses for a global user base. They need to select a vector store for the knowledge base. Which two options are fully managed AWS services suitable for this requirement? (Choose TWO.)
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 Aurora with pgvector extension
Amazon Aurora with pgvector extension is correct because it provides a fully managed relational database with native vector similarity search capabilities, enabling low-latency retrieval for generative AI applications. Amazon OpenSearch Serverless is correct because it offers a fully managed, serverless vector database with built-in k-NN search, ideal for global, low-latency knowledge base queries.
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.
- ✗
Pinecone
Why it's wrong here
Pinecone is a third-party vector database, not an AWS managed service.
- ✓
Amazon Aurora with pgvector extension
Why this is correct
Amazon Aurora supports pgvector for vector storage and search, and is fully managed.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
MongoDB Atlas
Why it's wrong here
MongoDB Atlas is a third-party service, not an AWS managed service.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB is a key-value and document database, but does not natively support vector search for production RAG (no pgvector equivalent).
- ✓
Amazon OpenSearch Serverless
Why this is correct
Amazon OpenSearch Serverless provides a managed vector engine for low-latency similarity search.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between AWS fully managed services and third-party managed services, where candidates mistakenly assume any managed vector store qualifies, but only AWS-native services like Aurora with pgvector and OpenSearch Serverless meet the 'fully managed AWS services' criterion.
Detailed technical explanation
How to think about this question
Amazon Aurora with pgvector uses the IVFFlat or HNSW indexing algorithms for approximate nearest neighbor (ANN) search, balancing recall and latency. Amazon OpenSearch Serverless leverages the nmslib and faiss libraries under the hood for k-NN search, automatically scaling compute and storage based on query volume, which is critical for global, low-latency inference pipelines.
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
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 Aurora with pgvector extension — Amazon Aurora with pgvector extension is correct because it provides a fully managed relational database with native vector similarity search capabilities, enabling low-latency retrieval for generative AI applications. Amazon OpenSearch Serverless is correct because it offers a fully managed, serverless vector database with built-in k-NN search, ideal for global, low-latency knowledge base queries.
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.
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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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