1Z0-1127-25 OCI Generative AI Service Practice Question
A company wants to use OCI Generative AI to build a multilingual customer support chatbot. They need to understand customer queries in multiple languages and generate responses in the same language. Which TWO actions should they take? (Choose 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
✓
Select Cohere Command R as the base model for chat
Cohere Command R (or R+) supports multilingual input and output natively, so fine-tuning is unnecessary. Using the embed-multilingual-v3.0 model for embedding customer queries enables multilingual semantic search if RAG is used. Embed-english-v3.0 only supports English, and Llama 3 is primarily English-focused.
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 embed-english-v3.0 model for embedding queries
Why it's wrong here
Embed-english-v3.0 only supports English; multilingual requires embed-multilingual-v3.0.
- ✗
Fine-tune Meta Llama 3 on multilingual data
Why it's wrong here
Llama 3 is primarily English; fine-tuning for multilingual support is less effective than using a natively multilingual model.
- ✓
Select Cohere Command R as the base model for chat
Why this is correct
Command R supports multilingual conversations without additional fine-tuning.
- ✓
Use the embed-multilingual-v3.0 model for embedding queries
Why this is correct
Multilingual embedding model is needed for non-English queries.
- ✗
Use the Summarisation API for each language
Why it's wrong here
Summarisation is for summarization, not for multilingual chat.
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