AI-102 Implement generative AI solutions Practice Question
You are designing a generative AI solution using Azure OpenAI Service. The solution must support multiple languages and provide consistent quality across languages. Which THREE actions should you take?
⚠ Common exam trap
It's easy for candidates to think fine-tuning on a single language (Option A) is sufficient for multilingual support, or that setting temperature to 0 (Option D) universally improves consistency, when in fact these actions undermine the required cross-lingual quality and flexibility.
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 model that supports multiple languages (e.g., GPT-4)
GPT-4 is a multilingual model pre-trained on diverse language corpora, enabling it to generate coherent and contextually appropriate responses across many languages without additional fine-tuning. This ensures consistent quality by leveraging the model's inherent cross-lingual capabilities, which is essential for a generative AI solution that must support multiple languages.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune the model on a dataset of a single language
Why it's wrong here
Fine-tuning on one language may reduce performance on others.
- ✓
Use a model that supports multiple languages (e.g., GPT-4)
Why this is correct
Multilingual models handle multiple languages natively.
- ✓
Provide examples in multiple languages in the prompt
Why this is correct
Few-shot examples guide the model to produce consistent output in each language.
- ✗
Set the temperature to 0 for all requests
Why it's wrong here
Setting temperature to 0 reduces creativity but does not ensure consistency across languages.
- ✓
Test the solution with representative prompts in each language
Why this is correct
Testing ensures quality across languages.
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