1Z0-1127-25 Using OCI Generative AI Service Practice Question
A company is deploying a multi-language chatbot using OCI Generative AI Service. The chatbot must support English, Spanish, and French. The team finds that responses in Spanish are less accurate than in English. They have a small bilingual dataset. What is the best approach?
⚠ Common exam trap
The trap here is that candidates often overestimate the power of prompt engineering (Option B) for language-specific accuracy, underestimating that systematic linguistic errors require model adaptation through fine-tuning or transfer learning, not just instruction tuning.
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 multilingual base model (e.g., mT5) and fine-tune on the bilingual dataset (English and Spanish) using cross-lingual transfer learning.
Fine-tuning a multilingual base model like mT5 on a small bilingual dataset leverages cross-lingual transfer learning, where knowledge from high-resource languages (English) improves performance on low-resource languages (Spanish). This approach is specifically designed for scenarios with limited data and directly addresses the accuracy gap without requiring separate models or translation pipelines.
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 a multilingual base model (e.g., mT5) and fine-tune on the bilingual dataset (English and Spanish) using cross-lingual transfer learning.
Why this is correct
Cross-lingual transfer leverages English data to improve Spanish performance, and fine-tuning on bilingual data further boosts accuracy.
- ✗
Use prompt engineering with language-specific instructions in the system prompt.
Why it's wrong here
Prompt engineering may not be enough if the base model has limited Spanish capabilities.
- ✗
Translate all user queries to English, process them, then translate responses back.
Why it's wrong here
This adds latency and potential translation errors; it's not optimal for real-time chatbots.
- ✗
Train separate fine-tuned models for each language.
Why it's wrong here
This is resource-intensive and not necessary; a multilingual model can handle all languages.
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