MLA-C01 ML Model Development Practice Question
A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?
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 Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store
RAG allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining.
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 larger foundation model with a longer context window and paste all documents into each prompt
Why it's wrong here
Pasting all documents into every prompt exceeds practical context limits and costs, and monthly updates would require re-embedding the entire corpus each time. Long-context prompting suits one-off analysis of a fixed, small document set, not a maintained knowledge base queried repeatedly.
- ✓
Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store
Why this is correct
RAG retrieves relevant passages from the indexed policy documents at query time and supplies them as context to the model, so monthly updates only require re-indexing the vector store rather than retraining. This satisfies the constraint that retraining is unaffordable.
- ✗
Fine-tune a base LLM on the policy documents monthly
Why it's wrong here
Fine-tuning bakes policy content into model weights, so monthly document changes force a fresh training run, exactly the retraining cost the team cannot afford. Fine-tuning suits teaching a model a fixed style or domain vocabulary, not keeping answers current against changing source documents.
- ✗
Train a custom model from scratch on the policy documents each month
Why it's wrong here
Training from scratch each month demands labelled data, GPU time and ML expertise, far exceeding the stated budget, and still freezes knowledge at training time. Building a custom model suits specialised domains needing bespoke architecture, not a chatbot answering questions over a small, changing document set.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.