For Azure OpenAI fine-tuning, training data must be provided as a JSON Lines (.jsonl) file. Each line represents one example. Because your data is described as prompt-completion pairs, the required shape is typically:
{ " prompt " : " < your prompt > " , " completion " : " < ideal completion > " } (Chat model fine-tuning instead uses a messages array per line, but the file format is still JSONL.) JSONL is the only accepted format for upload to the fine-tuning job.
Microsoft References
Azure OpenAI Service - Fine-tuning: data preparation and formats (JSONL; prompt-completion and chat messages ).
Azure OpenAI Service - How to fine-tune models (training files must be .jsonl).

Your app uses the primary admin key for index management. If it's suspected compromised, the least- downtime rotation is:
Regenerate the secondary admin key to ensure it's known only to you.
Switch the app to use the secondary key (no downtime-primary still works until you cut it off).
Regenerate the primary key to invalidate the compromised credential.
Query keys are read-only and cannot manage indexes, so switching to a query key would not meet the requirement. Regenerating the primary first would break the app until you update it.
References (Microsoft Azure Cognitive Search)
Admin (primary/secondary) vs. query keys and key rotation; key regeneration invalidates only the selected key.
Guidance to use the alternate admin key while rotating the other to avoid downtime.

Obtain values to connect your bot to the knowledge base
1. In the QnA Maker site, select your knowledge base.
2. With your knowledge base open, select the SETTINGS tab. Record the value shown for service name. This value is useful for finding your knowledge base of interest when using the QnA Maker portal interface. It's not used to connect your bot app to this knowledge base.
3. Scroll down to find Deployment details and record the following values from the Postman sample HTTP request:
4. POST /knowledgebases/<knowledge-base-id>/generateAnswer
5. Host: <your-host-url>
6. Authorization: EndpointKey <your-endpoint-key>
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-howto-qna

Explanation:
Data Analyst.
When dealing with data roles in an organization, responsibilities are divided among data analysts, data engineers, and data scientists. Let's review them:
Data Analyst
Focuses on understanding business requirements and ensuring that the right rules and logic are applied to data so it becomes meaningful for decision-making.
They work closely with stakeholders to define business rules such as data validation rules, aggregation logic, and reporting metrics.
This makes them responsible for identifying which business rules must be applied to data.
Data Engineer
Responsible for designing, building, and maintaining the infrastructure and pipelines that move and process data.
They ensure data availability, reliability, and scalability, but do not usually define business rules.
Data Scientist
Focuses on advanced analytics, predictive modeling, and machine learning.
They use data prepared by engineers and analysts but are not typically the ones who define the business rules that govern how raw data should be interpreted.
Therefore, the correct answer is Data Analyst.
The Answer: A data analyst
Microsoft References
Data roles and responsibilities in Azure
What is a Data Analyst?
Responsibilities of Data Engineers vs Data Scientists vs Data Analysts