Databricks Certified Data Engineer - Associate

Explanation

Correct Answer: D - Cron syntax

In Databricks, when you need to programmatically represent and submit job schedules, you can use Cron syntax. This is because:

1. Databricks Jobs API supports Cron expressions: When creating or updating jobs via the Databricks REST API, you can specify the schedule using standard Cron syntax.

2. Easy transferability: Cron syntax provides a standardized way to represent complex schedules that can be easily copied and reused across different jobs.

3. Programmatic control: Unlike manually selecting values in the UI form, Cron syntax allows you to define schedules in code, making it suitable for Infrastructure as Code (IaC) practices and automation.

Why the 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: Similar to DateType, this is a data type for timestamps, not a scheduling tool.
• E. There is no way to represent and submit this information programmatically: This is incorrect because Databricks provides APIs that accept Cron syntax for scheduling.

Example of Cron syntax in Databricks API:

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

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

This example schedules a job to run daily at 9:00 AM in the specified timezone.