1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search
A team fine-tunes an embedding model for a legal document RAG system but observes low retrieval recall. Which technique is most likely to improve recall?
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 hard negative mining during training
Hard negative mining exposes the model to challenging negatives during training, which sharpens the embedding space and improves recall.
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 smaller batch size
Why it's wrong here
Batch size affects training stability, not recall directly.
- ✓
Use hard negative mining during training
Why this is correct
Hard negatives force the model to differentiate between similar but irrelevant documents, improving retrieval discrimination.
- ✗
Reduce the learning rate
Why it's wrong here
Lower learning rate may slow convergence but won't directly fix low recall.
- ✗
Increase the number of fine-tuning epochs
Why it's wrong here
More epochs may lead to overfitting, not necessarily improved recall.
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