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AIF-C01 Practice Question: A startup is building a semantic search system…
A startup is building a semantic search system over their product catalog using Amazon Bedrock. They want to convert product descriptions into vector embeddings and store them in a vector database for similarity search. Which TWO actions should they take? (Select TWO.)
⚠ Common exam trap
AWS often tests the distinction between generative models (like Claude) and embedding models (like Titan Embeddings), so candidates mistakenly think any Bedrock model can produce embeddings via InvokeModel, but only specific embedding models output vectors.
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
✓
Store the embeddings in a vector database such as Amazon OpenSearch Serverless with the k-NN plugin
Option E is correct because Amazon Titan Embeddings (e.g., amazon.titan-embed-text-v1/v2) is a purpose-built Bedrock text embedding model that converts product descriptions into dense vector embeddings suitable for semantic similarity search. Option A is correct because Amazon OpenSearch Serverless with the k-NN plugin is a managed vector database that supports storing embeddings and running approximate nearest-neighbor (ANN) similarity queries, which is exactly what the semantic search system requires. Option B is wrong because Titan Image Generator produces images from prompts, not embeddings, and it would not generate text-description embeddings. Option C is wrong because Anthropic Claude is a text generation/reasoning model on Bedrock and does not expose an embeddings API via InvokeModel. Option D is wrong because DynamoDB is a key-value/document store without native vector similarity search, so storing vectors as binary attributes would not support k-NN retrieval.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Store the embeddings in a vector database such as Amazon OpenSearch Serverless with the k-NN plugin
Why this is correct
Amazon OpenSearch Serverless with the k-NN plugin provides the vector index that stores embeddings and performs approximate nearest-neighbour similarity search, satisfying the stem's requirement for a vector database. Bedrock generates the embeddings; this service persists and queries them, returning semantically similar product descriptions by distance in vector space.
- ✗
Use Amazon Titan Image Generator to create embeddings for each product image
Why it's wrong here
Titan Image Generator produces images from text prompts; it returns no vector embeddings, so nothing can be stored for similarity search. It is tempting because Titan multimodal models do handle product imagery, and would be correct if the catalogue required generating or editing product pictures.
- ✗
Use Amazon Bedrock InvokeModel with Anthropic Claude to generate embeddings
Why it's wrong here
Claude is a text-generation model on Bedrock and does not expose an embeddings API, so InvokeModel cannot return the vectors needed for similarity search. It is tempting because InvokeModel is the correct Bedrock call pattern, and would be correct where the task is generating or summarising catalogue copy rather than embedding it.
- ✗
Store the embedding vectors in an Amazon DynamoDB table as binary attributes
Why it's wrong here
DynamoDB stores binary attributes but provides no native vector indexing or approximate-nearest-neighbour similarity search, so queries would require full scans. It is tempting because DynamoDB is a familiar, scalable key-value store, yet it is the correct choice for transactional metadata lookups, not embedding similarity retrieval.
- ✓
Use Amazon Titan Embeddings to generate vector embeddings from product descriptions
Why this is correct
Amazon Titan Embeddings converts text into dense numeric vectors, which is the required transformation before storing product descriptions in a vector database for similarity search. It satisfies the semantic search requirement by producing embeddings that capture meaning rather than keyword matches.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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