AI0-001 Implementing AI Solutions Practice Question

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-tuning, because it allows the model to internalize the statutes for faster inference

  Why it's wrong here

  Fine-tuning would require retraining every quarter, which is costly and time-consuming, and the model may still not handle updates between retraining.

• ✗

  Fine-tuning with LoRA, because it is parameter-efficient and can be updated frequently

  Why it's wrong here

  Even LoRA requires retraining on each update; managing quarterly retraining cycles is still overhead compared to RAG.

• ✓

  RAG, because it can retrieve the latest documents without retraining the model

  Why this is correct

  RAG indexes the latest documents and retrieves relevant chunks at query time, ensuring answers are based on current statutes without any model retraining.

• ✗

  RAG with fine-tuning on the initial statutes, then update the index quarterly

  Why it's wrong here

  Fine-tuning on the initial statutes, then updating only the index quarterly, fails because the model’s parametric knowledge remains frozen after the initial fine-tuning; it cannot incorporate the quarterly statutory changes into its internal weights. The retrieval index may hold the new text, but the model lacks the updated reasoning patterns needed to interpret novel regulations correctly. This approach is tempting because it combines the precision of fine-tuning with the freshness of RAG, and would be correct if the statutes changed only once, not repeatedly.