A Fabric notebook ingestion process must run daily at 7:00 AM and retry up to two times if source missing; minimal development effort. Which approach meets this?

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Multiple Choice

A Fabric notebook ingestion process must run daily at 7:00 AM and retry up to two times if source missing; minimal development effort. Which approach meets this?

Explanation:
The key idea here is to orchestrate a daily ingestion with a single, reusable workflow that can handle automatic retries. Put Notebook1 into a pipeline and schedule that pipeline to run every day at 7:00 AM. This creates one centralized process you can manage and monitor, and you can configure a retry policy on the pipeline so that if the source is missing, it will automatically retry up to two times. This approach satisfies the “minimal development effort” requirement because you avoid duplicating notebooks or building multiple separate run configurations. Adding the notebook three times with dependencies would add unnecessary complexity and maintenance overhead. Relying on the Notebook’s own scheduling alone can work, but it scatters scheduling and retry behavior across notebooks instead of keeping them in one place. Creating separate notebooks for each run and time segment multiplies artifacts and makes updates harder to propagate.

The key idea here is to orchestrate a daily ingestion with a single, reusable workflow that can handle automatic retries. Put Notebook1 into a pipeline and schedule that pipeline to run every day at 7:00 AM. This creates one centralized process you can manage and monitor, and you can configure a retry policy on the pipeline so that if the source is missing, it will automatically retry up to two times. This approach satisfies the “minimal development effort” requirement because you avoid duplicating notebooks or building multiple separate run configurations.

Adding the notebook three times with dependencies would add unnecessary complexity and maintenance overhead. Relying on the Notebook’s own scheduling alone can work, but it scatters scheduling and retry behavior across notebooks instead of keeping them in one place. Creating separate notebooks for each run and time segment multiplies artifacts and makes updates harder to propagate.

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