Generative AI Leader Google Cloud's Generative AI Offerings Practice Question
A multinational corporation is using Vertex AI to generate multilingual customer support responses. They have fine-tuned the Gemini model on support tickets in English and now want to extend to 10 additional languages. The fine-tuning dataset for new languages is small (1000 tickets each). During evaluation, the model performs well for common languages (Spanish, French) but poorly for languages like Finnish and Thai. The team needs to improve performance for low-resource languages. They have budget constraints and cannot collect more data quickly. Which approach should they take?
⚠ Common exam trap
Watch out — candidates often assume more data (Option D) or separate models (Option C) are the only solutions, ignoring that cross-lingual transfer learning can effectively bootstrap low-resource languages from high-resource ones without additional data collection.
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 foundation model and fine-tune with cross-lingual transfer learning techniques.
Using a multilingual foundation model (like Gemini's multilingual variant) with cross-lingual transfer learning leverages the model's pre-trained knowledge across languages, allowing it to generalize from high-resource languages (Spanish, French) to low-resource ones (Finnish, Thai) even with small fine-tuning datasets. This approach is budget-friendly as it avoids separate models or costly data collection, and it directly addresses the performance gap by sharing linguistic patterns across 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.
- ✗
Switch to Vertex AI Codey API for generating responses in all languages.
Why it's wrong here
Codey is a code-generation model, not a multilingual customer-support text generator, so it cannot produce Finnish or Thai responses regardless of tuning. It would be correct for code completion and code-related tasks, not for extending Gemini's multilingual support capability.
- ✓
Use a multilingual foundation model and fine-tune with cross-lingual transfer learning techniques.
Why this is correct
Cross-lingual transfer learning leverages the multilingual foundation model's shared representations, so the 1,000-ticket datasets per language transfer knowledge from the English fine-tune. This lifts Finnish and Thai performance without collecting more data, satisfying the budget constraint.
- ✗
Deploy separate fine-tuned models for each language.
Why it's wrong here
Ten separate fine-tuned models multiply training, hosting and maintenance cost, and each still trains on the same sparse 1000-ticket sets, so Finnish and Thai quality does not improve. Per-language models suit scenarios with abundant, isolated data and no shared multilingual representation.
- ✗
Collect more training data for low-resource languages via crowdsourcing.
Why it's wrong here
Crowdsourcing more tickets conflicts with the stated budget and speed constraints, and 1000 tickets per language already exist. It would be correct if time and funding allowed dataset expansion. The low-resource gap is better addressed by cross-lingual transfer or adapter-based tuning rather than new collection.
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