Databricks Certified Data Engineer - Associate

Explanation

Correct Answer: D. Cron syntax

In Databricks, job schedules can be represented and submitted programmatically using Cron syntax. This allows data engineers to:

1. Programmatically define schedules using the Databricks Jobs API or Terraform
2. Transfer complex schedules between jobs without manual configuration
3. Use standard cron expressions to specify minute, hour, day of month, month, and day of week patterns

Why other options are incorrect:

• A. pyspark.sql.types.DateType: This is a Spark SQL data type for representing dates, not a scheduling mechanism.
• B. datetime: This is a Python module for working with dates and times, but not specifically for defining job schedules in Databricks.
• C. pyspark.sql.types.TimestampType: This is a Spark SQL data type for timestamps, not a scheduling tool.
• E. There is no way to represent and submit this information programmatically: This is incorrect - Databricks provides APIs and tools for programmatic job scheduling.

How to use Cron syntax in Databricks:

When creating or updating jobs via the Databricks Jobs API, you can specify the schedule using cron expressions:

{
  "schedule": {
    "quartz_cron_expression": "0 0 9 * * ?",
    "timezone_id": "America/Los_Angeles"
  }
}

{
  "schedule": {
    "quartz_cron_expression": "0 0 9 * * ?",
    "timezone_id": "America/Los_Angeles"
  }
}

Or using Terraform:

resource "databricks_job" "example" {
  schedule {
    quartz_cron_expression = "0 0 9 * * ?"
    timezone_id = "America/Los_Angeles"
  }
}

resource "databricks_job" "example" {
  schedule {
    quartz_cron_expression = "0 0 9 * * ?"
    timezone_id = "America/Los_Angeles"
  }
}

This approach enables automation, version control, and consistent deployment of job schedules across different environments.