Facing AI-102 exam challenges in fine-tuning Azure OpenAI? Discover how to prepare your data in JSON Lines format and customize your chatbot for accurate customer support. Get the edge you need!
Table of Contents
Question
Your organization, Xerigon Corporation, is developing a customer service chatbot using Azure OpenAI to provide accurate responses to specific inquiries about your company’s products and policies.
While testing the model, it was observed that the responses from the Azure AI model were not fully aligned with your company’s specific terminology and unique support needs.
To improve this, you decide to fine-tune the model by training it on past customer-support transcripts and policy documents.
What is the first step you should take to begin fine-tuning the Azure OpenAI model with your custom data?
A. Choose the training data for your Azure AI model.
B. Directly train the model with your support transcripts in the Azure AI Studio interface.
C. Prepare your dataset in JSON Lines format, and upload it to Azure Blob Storage.
D. Check the status of your custom fine-tuned Azure AI model.
Answer
C. Prepare your dataset in JSON Lines format, and upload it to Azure Blob Storage.
Explanation
The first step in fine-tuning an Azure OpenAI model is to prepare your training data in JSON Lines (JSONL) format. This format ensures that each line of the file represents a prompt-completion pair, which is essential for supervised fine-tuning. Once the dataset is ready, it would be uploaded to Azure Blob Storage which serves as the data source for the fine-tuning process.
You would follow the below-outlined fine-tuning workflow using Azure AI Studio:
- Prepare your training and validation data.
- In Azure AI Studio, use the Create custom wizard to train your custom model:
- Select the base model.
- Choose your training data or upload prepared training data.
- Choose your validation data. (This is an optional step.)
- Configure task parameters for the fine-tuning job. (This is an optional step.)
- Review your inputs.
- Train your new custom model.
- Monitor the status of your fine-tuning process.
- Deploy your custom model for practical use.
- Utilize your custom model.
- Optionally, analyze your model’s performance and suitability for specific tasks.
Directly training the model with your support transcripts in the Azure AI Studio interface is not the first step in the given scenario. Azure AI Studio offers tools for managing and deploying models. However, it does not allow for direct training using raw transcripts.
Choosing the training data for your Azure AI model is not the first step in the given scenario. This step is included when you are training the custom model using the Create custom wizard as explained in the above fine-tuning workflow.
Checking the status of your custom fine-tuned Azure AI model is not the first step in the given scenario. Monitoring the status of a fine-tuned model is crucial to track its progress and ensure that it is ready for deployment. However, this step occurs after the fine-tuning process has been initiated and is not relevant at the beginning.
Microsoft Azure AI Engineer Associate AI-102 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Engineer Associate AI-102 exam and earn Microsoft Azure AI Engineer Associate AI-102 certification.