Which health checks are recommended to install on output datasets of a Foundry data pipeline? Select three.

In the context of health checks for output datasets of a Foundry data pipeline, the recommended checks serve distinct purposes that enhance the reliability and integrity of the data being processed.

The schema check is critical because it verifies that the output dataset adheres to a defined structure and format. This ensures that any downstream consumers of the data can expect the data to conform to specific standards, reducing the likelihood of errors when the data is utilized in further analyses or applications. By ensuring that the dataset contains the correct fields, data types, and even constraints, schema checks help maintain data quality, thus fostering trust in the data outputs.

While the other checks such as build status, build duration, and sync status are useful for monitoring the operational aspects of the data pipeline, they do not directly assess the validity and quality of the data itself. The build status check indicates whether a data pipeline has successfully completed its job, build duration measures how long the data processing takes, and sync status pertains to the synchronization of data across systems. While all of these factors are important for monitoring the pipeline's health and performance, the schema check is essential for ensuring the integrity of the actual data output, which is why it stands out as a key health check.

In summary, by implementing a