easyMultiple Select
AIF-C01 Practice Question: A developer is building an application that uses…
A developer is building an application that uses Amazon Bedrock to answer questions based on a large internal knowledge base. The knowledge base contains PDFs, Word documents, and web pages. Which TWO AWS services are commonly used together to implement a Retrieval-Augmented Generation (RAG) architecture on AWS? (Select TWO.)
⚠ Common exam trap
Many exam-takers confuse data preparation or query services (like AWS Glue or Athena) with the vector search and retrieval components essential for RAG, overlooking that Amazon OpenSearch Serverless provides the vector database capability while Bedrock Knowledge Bases orchestrates the ingestion and retrieval pipeline.
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 Bedrock Knowledge Bases
Amazon Bedrock Knowledge Bases (A) is correct because it is the managed RAG capability that ingests the PDFs, Word documents, and web pages, chunks and embeds them, and orchestrates retrieval so the foundation model can answer questions grounded in the internal knowledge base. Amazon OpenSearch Serverless (E) is correct because it is the commonly used vector store backing a Bedrock knowledge base, holding the embeddings and performing the vector similarity search that retrieves relevant passages at query time. Together they form the standard AWS RAG pattern: Bedrock Knowledge Bases for ingestion, embedding, and orchestration, and OpenSearch Serverless as the vector index for semantic retrieval. Amazon SageMaker Ground Truth (B) is a data-labeling service for building training datasets, not a retrieval component. AWS Glue (C) is a serverless ETL/catalog service and Amazon Athena (D) is a serverless SQL query engine over S3; neither provides the vector similarity search or managed RAG orchestration this scenario requires.
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 Bedrock Knowledge Bases
Why this is correct
Amazon Bedrock Knowledge Bases handles the ingestion, chunking and embedding of the PDFs, Word documents and web pages into a vector store, then retrieves the relevant passages at query time so the model answers from your internal corpus rather than its training data. This directly satisfies the RAG requirement for grounding responses in the knowledge base.
- ✗
Amazon SageMaker Ground Truth
Why it's wrong here
Ground Truth labels training data for custom models; it neither chunks nor indexes documents for retrieval, so Bedrock cannot query the knowledge base. It is the right choice when building labelled datasets for supervised fine-tuning, not for supplying source passages at inference time.
- ✗
AWS Glue
Why it's wrong here
Glue performs ETL and cataloguing for analytics pipelines; it does not generate embeddings or serve similarity search, so Bedrock cannot retrieve passages. Glue is correct when transforming and cataloguing raw data for Athena or Redshift queries, not for RAG retrieval.
- ✗
Amazon Athena
Why it's wrong here
Athena runs SQL over data in Amazon S3; it returns tabular result sets, not semantically ranked passages, and produces no embeddings for Bedrock to query. Athena fits ad-hoc SQL analytics on catalogued S3 data, whereas RAG needs a vector store and embedding model.
- ✓
Amazon OpenSearch Serverless
Why this is correct
Amazon OpenSearch Serverless provides the vector store underpinning RAG, holding embeddings of the PDFs, Word documents and web pages so Bedrock can retrieve semantically relevant passages at query time. Its vector search collection type scales without cluster management, satisfying the knowledge-base retrieval requirement alongside Amazon Bedrock's generation.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.