A data engineer needs to configure Cloud Scheduler to trigger a downstream HTTP endpoint that initiates a weekly BigQuery data aggregation job. The workflow has the following operational requirements: The job must execute automatically every Sunday at 1:00 AM. The schedule must follow Coordinated Universal Time (UTC) to avoid daylight saving time irregularities. If the target endpoint fails or returns a transient error code, the system must automatically re-attempt execution with increasing delays between attempts. Which Cloud Scheduler configuration satisfies these requirements?

Set the schedule to `0 1 * * 0`, select the UTC time zone, and configure the job's retry policy with exponential backoff.: What Cloud Scheduler and its configuration are Cloud Scheduler is a fully managed enterprise cron job service that enables automated, periodic execution of units of work across Google Cloud services and external endpoints. A Cloud Scheduler job combines a target destination (such as an HTTP/S endpoint, Pub/Sub topic, or App Engine application), a execution frequency defined using standard cron expressions, and execution parameters including time zone and retry policies. How it meets the requirements Weekly execution on Sunday at 1:00 AM (0 1 * * 0): Standard unix-cron syntax consists of five fields: minute (0), hour (1), day of month (), month (), and day of week (0 for Sunday). This pattern ensures that the job triggers precisely at 01:00 every Sunday. UTC time zone: Specifying Coordinated Universal Time (UTC) ensures consistent execution intervals that are completely immune to irregularities caused by daylight saving time (DST) transitions. Handling transient failures: Configuring a retry policy allows Cloud Scheduler to automatically retry unacknowledged or failed requests using exponential backoff, spacing out subsequent attempts to allow downstream services to recover from transient faults. Key advantages and capabilities Standard cron syntax: Supports familiar 5-field unix-cron expressions for precise recurring schedules. Automated exponential backoff: Adjusts retry intervals dynamically using minimum and maximum backoff duration thresholds. Time zone flexibility: Allows explicit time zone definitions while supporting UTC as the standard reference. Target versatility: Directly invokes HTTP/S targets, Google Cloud Pub/Sub topics, or App Engine services without dedicated compute servers. Idempotency support: Provides headers such as X-CloudScheduler-ScheduleTime to assist downstream targets in avoiding duplicate execution side effects. Why this is optimal This configuration leverages Cloud Scheduler's native scheduling syntax and built-in error handling. By combining standard unix-cron scheduling, UTC alignment, and exponential backoff retry settings, the pipeline achieves dependable automated triggering without requiring external orchestration infrastructure or custom retry scripts.

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Weekly Cloud Scheduler Jobs With Exponential Backoff

Section: Create and manage scheduled queries (e.g., BigQuery, Cloud Scheduler, Cloud Composer)

A data engineer needs to configure Cloud Scheduler to trigger a downstream HTTP endpoint that initiates a weekly BigQuery data aggregation job. The workflow has the following operational requirements:

The job must execute automatically every Sunday at 1:00 AM.
The schedule must follow Coordinated Universal Time (UTC) to avoid daylight saving time irregularities.
If the target endpoint fails or returns a transient error code, the system must automatically re-attempt execution with increasing delays between attempts.

Which Cloud Scheduler configuration satisfies these requirements?

Set the schedule to 1 0 * * 7, select the UTC time zone, and configure an attempt deadline of zero seconds with no retries.

Set the schedule to 0 1 * * 0, select the UTC time zone, and configure the job's retry policy with exponential backoff.

Set the schedule to 0 1 0 * *, select the America/New_York time zone, and enable linear retry polling.

Set the schedule to * 1 * * 0, select the UTC time zone, and set the maximum retry duration to unlimited with App Engine strict delivery.

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Fundamental

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Track Readiness

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Fundamental

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Track Readiness

Set Exam Goal

Weekly Cloud Scheduler Jobs With Exponential Backoff

Section: Create and manage scheduled queries (e.g., BigQuery, Cloud Scheduler, Cloud Composer)

The job must execute automatically every Sunday at 1:00 AM.
The schedule must follow Coordinated Universal Time (UTC) to avoid daylight saving time irregularities.
If the target endpoint fails or returns a transient error code, the system must automatically re-attempt execution with increasing delays between attempts.

Which Cloud Scheduler configuration satisfies these requirements?

Set the schedule to 1 0 * * 7, select the UTC time zone, and configure an attempt deadline of zero seconds with no retries.

Set the schedule to 0 1 * * 0, select the UTC time zone, and configure the job's retry policy with exponential backoff.

Set the schedule to 0 1 0 * *, select the America/New_York time zone, and enable linear retry polling.

Set the schedule to * 1 * * 0, select the UTC time zone, and set the maximum retry duration to unlimited with App Engine strict delivery.

Explanations

What Cloud Scheduler and its configuration are

Cloud Scheduler is a fully managed enterprise cron job service that enables automated, periodic execution of units of work across Google Cloud services and external endpoints. A Cloud Scheduler job combines a target destination (such as an HTTP/S endpoint, Pub/Sub topic, or App Engine application), a execution frequency defined using standard cron expressions, and execution parameters including time zone and retry policies.

How it meets the requirements

Weekly execution on Sunday at 1:00 AM (0 1 * * 0): Standard unix-cron syntax consists of five fields: minute (0), hour (1), day of month (*), month (*), and day of week (0 for Sunday). This pattern ensures that the job triggers precisely at 01:00 every Sunday.
UTC time zone: Specifying Coordinated Universal Time (UTC) ensures consistent execution intervals that are completely immune to irregularities caused by daylight saving time (DST) transitions.
Handling transient failures: Configuring a retry policy allows Cloud Scheduler to automatically retry unacknowledged or failed requests using exponential backoff, spacing out subsequent attempts to allow downstream services to recover from transient faults.

Key advantages and capabilities

Standard cron syntax: Supports familiar 5-field unix-cron expressions for precise recurring schedules.
Automated exponential backoff: Adjusts retry intervals dynamically using minimum and maximum backoff duration thresholds.
Time zone flexibility: Allows explicit time zone definitions while supporting UTC as the standard reference.
Target versatility: Directly invokes HTTP/S targets, Google Cloud Pub/Sub topics, or App Engine services without dedicated compute servers.
Idempotency support: Provides headers such as X-CloudScheduler-ScheduleTime to assist downstream targets in avoiding duplicate execution side effects.

Why this is optimal

This configuration leverages Cloud Scheduler's native scheduling syntax and built-in error handling. By combining standard unix-cron scheduling, UTC alignment, and exponential backoff retry settings, the pipeline achieves dependable automated triggering without requiring external orchestration infrastructure or custom retry scripts.

View source documentation

Associate Data Practitioner

Weekly Cloud Scheduler Jobs With Exponential Backoff

Associate Data Practitioner

Weekly Cloud Scheduler Jobs With Exponential Backoff

What Cloud Scheduler and its configuration are

How it meets the requirements

Key advantages and capabilities

Why this is optimal

BigQuery Dry Run

Cloud Composer DAG Task Dependencies

BigQuery ML ML.PREDICT

Cloud Composer

Cloud Storage Location Type Selection

Associate Data Practitioner

Weekly Cloud Scheduler Jobs With Exponential Backoff

Associate Data Practitioner

Weekly Cloud Scheduler Jobs With Exponential Backoff

What Cloud Scheduler and its configuration are

How it meets the requirements

Key advantages and capabilities

Why this is optimal

Related questions

BigQuery Dry Run

Cloud Composer DAG Task Dependencies

BigQuery ML ML.PREDICT

Cloud Composer

Cloud Storage Location Type Selection