Question
You are fine-tuning a base language model to analyze customer feedback.
You label examples of support tickets. You must improve classification accuracy by configuring and fine-tuning the base model in Microsoft Foundry.
You need to configure and run fine-tuning.
What should you do first?
- A. Use prompt flow to generate multiple prompt templates for evaluation.
- B. Deploy the base model to an online endpoint before starting fine-tuning.
- C. Enable tracing for all inference calls in the evaluation pipeline.
- D. Format the dataset as a JSONL file with prompt-completion pairs and upload the file. Correct
Explanation
A fine-tuning job needs its training data first: the labeled examples are saved as a JSONL file, each line pairing the input with the desired response, and uploaded so the job can reference it. The base model does not need to be deployed before fine-tuning; you deploy the fine-tuned result afterward. Prompt templates and tracing support prompt engineering and evaluation, not the setup of a fine-tuning job.
