1Z0-1127-25 OCI Generative AI Service Practice Question
A company wants to implement a retrieval-augmented generation (RAG) chatbot using OCI Generative AI Agents. Which TWO services or components are required for this solution?
⚠ Common exam trap
Many candidates assume a dedicated AI cluster or a specific embedding model is mandatory for RAG, when in fact OCI Generative AI Agents abstract away these details and only require a knowledge base and a data source like Object Storage.
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
✓
Knowledge Base
A Knowledge Base is the core repository that stores the documents or data sources the RAG chatbot retrieves from. OCI Generative AI Agents use a knowledge base to perform retrieval-augmented generation, where the agent first retrieves relevant chunks from the knowledge base and then passes them to the generative model to produce a grounded, context-aware response.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cohere Embed model
Why it's wrong here
Embedding is handled internally by the service, not a separate component you provision.
- ✗
Fine-tuned model
Why it's wrong here
Fine-tuning is not necessary for RAG; the base model is used.
- ✓
Knowledge Base
Why this is correct
The knowledge base indexes documents and enables retrieval.
- ✓
OCI Object Storage
Why this is correct
Used to store source documents for the knowledge base.
- ✗
Dedicated AI Cluster
Why it's wrong here
Not required; Agents can use shared infrastructure.
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