1Z0-1127-25 Using OCI Generative AI Service Practice Question
A startup is building a chatbot for customer support using OCI Generative AI Service. The chatbot needs to answer queries about product features based on a knowledge base of product documentation. Which configuration is most appropriate for this use case?
⚠ Common exam trap
Oracle often tests the distinction between task-specific models (summarization, code generation) and the RAG architecture, leading candidates to mistakenly choose a simpler task type like summarization instead of recognizing the need for retrieval-augmented generation.
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
✓
Use a Cohere Command model with the knowledge base as context in a prompt, and enable retrieval-augmented generation (RAG) via OCI Generative AI Agents.
OCI Generative AI Agents with retrieval-augmented generation (RAG) allows the chatbot to dynamically retrieve relevant chunks from the product documentation knowledge base and inject them as context into a Cohere Command model prompt. This approach ensures answers are grounded in the latest documentation without requiring fine-tuning, and it scales efficiently as the knowledge base grows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the Summarization task type to generate concise answers from the documentation.
Why it's wrong here
Summarization is not designed for question answering; it produces a summary of a block of text.
- ✓
Use a Cohere Command model with the knowledge base as context in a prompt, and enable retrieval-augmented generation (RAG) via OCI Generative AI Agents.
Why this is correct
This approach uses a foundation model with RAG to ground responses in the knowledge base, which is ideal for question answering.
- ✗
Fine-tune a Llama 2 70B model on the product documentation to create a custom model.
Why it's wrong here
Fine-tuning a Llama 2 70B model on product documentation is unnecessary and inefficient because the OCI Generative AI Service provides a managed Retrieval-Augmented Generation (RAG) architecture that injects relevant document chunks into the prompt context at inference time, avoiding the cost and latency of full model retraining. This option is tempting because fine-tuning is the correct approach when the knowledge base requires the model to learn new reasoning patterns or domain-specific language that cannot be captured by prompt-based retrieval alone.
- ✗
Use the Code Generation model to produce SQL queries that retrieve answers from a database.
Why it's wrong here
Code generation is not relevant; the use case is text-based Q&A.
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