- A
A vector database such as Pinecone or Weaviate
Vector databases are optimized for storing and querying embeddings with approximate nearest neighbor search.
- B
An embedding model from Hugging Face Transformers
The embedding model converts text into vector embeddings that are stored in the vector database.
- C
A relational database with BLOB storage
Why wrong: Relational databases lack vector search capabilities and are inefficient for similarity search.
- D
A GPU cluster for model serving
Why wrong: RAG retrieval does not require GPU; the embedding model may run on CPU for retrieval, and GPU is for the generation model.
- E
A data warehouse like Snowflake or BigQuery
Why wrong: Data warehouses are for analytical queries on structured data, not for vector search.
Question 512 of 1,000
AI0-001 AI Infrastructure and Technologies Practice Question
This AI0-001 practice question tests your understanding of ai infrastructure and technologies. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 building a RAG (Retrieval-Augmented Generation) system. They need to store document embeddings and retrieve relevant chunks efficiently. Which TWO technologies are most appropriate for this task? (Select 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
✓
A vector database such as Pinecone or Weaviate
A vector database such as Pinecone or Weaviate is specifically designed to store and index high-dimensional vector embeddings, enabling efficient approximate nearest neighbor (ANN) search. This is essential for RAG systems to quickly retrieve the most semantically relevant document chunks based on the query embedding.
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.
- ✓
A vector database such as Pinecone or Weaviate
Why this is correct
Vector databases are optimized for storing and querying embeddings with approximate nearest neighbor search.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
An embedding model from Hugging Face Transformers
Why this is correct
The embedding model converts text into vector embeddings that are stored in the vector database.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A relational database with BLOB storage
Why it's wrong here
Relational databases lack vector search capabilities and are inefficient for similarity search.
- ✗
A GPU cluster for model serving
Why it's wrong here
RAG retrieval does not require GPU; the embedding model may run on CPU for retrieval, and GPU is for the generation model.
- ✗
A data warehouse like Snowflake or BigQuery
Why it's wrong here
Data warehouses are for analytical queries on structured data, not for vector search.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between the component that generates embeddings (the model) and the component that stores/retrieves them (the vector database), leading candidates to mistakenly select only one or to confuse a data warehouse with a vector store.
Trap categories for this question
Similar concept trap
Relational databases lack vector search capabilities and are inefficient for similarity search.
Detailed technical explanation
How to think about this question
Vector databases implement specialized indexing structures such as HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index) to perform approximate nearest neighbor search in O(log n) time, far outperforming brute-force linear scans. In a RAG pipeline, the embedding model (e.g., from Hugging Face) generates the vectors, but the vector database handles persistence and retrieval; both are needed but serve distinct roles.
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 network engineer at a university connects two campus buildings via a fibre link. Both routers run OSPF, but no adjacency forms — even though both routers can ping each other. The engineer finds one router is in area 0 and the other in area 1. OSPF adjacency requires matching area numbers, hello/dead timers, and network type. IP reachability alone is not enough.
What to study next
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Infrastructure and Technologies — This question tests AI Infrastructure and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: A vector database such as Pinecone or Weaviate — A vector database such as Pinecone or Weaviate is specifically designed to store and index high-dimensional vector embeddings, enabling efficient approximate nearest neighbor (ANN) search. This is essential for RAG systems to quickly retrieve the most semantically relevant document chunks based on the query embedding.
What should I do if I get this AI0-001 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 AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.
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