1Z0-1127-25 LLM Fundamentals Practice Question
An organization wants to use OCI Generative AI for a multilingual translation task. They need high quality and must avoid biases present in the training data. Which THREE strategies should they consider? (Select THREE.)
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
✓
Implement a human-in-the-loop review process to catch biased translations
Fine-tuning on high-quality parallel corpora improves accuracy. Using models designed for translation (e.g., encoder-decoder) often yields better results. Implementing human-in-the-loop review catches biases. Increasing temperature may reduce bias but also reduces quality; it is not a primary strategy for bias mitigation. RAG is not directly applicable to translation as it requires retrieved documents in the target language.
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 RAG pipeline to retrieve canonical translations from a trusted database
Why it's wrong here
RAG is not typically used for translation; retrieval would require a database of translations, which is not standard.
- ✓
Implement a human-in-the-loop review process to catch biased translations
Why this is correct
Human review is an effective way to identify and correct biased outputs.
- ✓
Fine-tune a pre-trained model on a high-quality parallel corpus for the target language pairs
Why this is correct
Fine-tuning improves translation quality and can reduce domain-specific biases.
- ✗
Increase the temperature parameter to 1.5 to reduce repetitive biases
Why it's wrong here
Increasing temperature may introduce randomness and reduce quality, not specifically address bias.
- ✓
Use an encoder-decoder model such as T5 or BART
Why this is correct
Encoder-decoder architectures are typically better for translation than decoder-only models.
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