Cloud Run jobs for batch work in GCP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ service_behavior

intermediate

How does Cloud Run Jobs handle container execution?

Cloud Run Jobs run containers to perform batch work. What happens when a job container finishes its task?

A The container instance stays running to accept new requests.

B The container instance is immediately terminated and the job is marked complete.

C The container instance restarts automatically to run the job again.

D The container instance is paused and resumed later for the next batch.

💡 Hint

Think about batch jobs that run once and then stop.

Cloud Run Jobs run containers that execute a batch task once. When the container finishes, it stops and the job is marked complete. It does not stay running or restart automatically.

❓ Architecture

intermediate

Choosing Cloud Run Jobs for batch workloads

You have a batch workload that processes files uploaded to Cloud Storage once per day. Which architecture best uses Cloud Run Jobs?

A Trigger a Cloud Run Job daily via Cloud Scheduler to process all new files.

B Deploy a Cloud Run service that listens to HTTP requests and processes files on demand.

C Use Cloud Functions triggered by Cloud Storage events to process each file individually.

D Run a Compute Engine VM that polls Cloud Storage every hour to process files.

💡 Hint

Think about scheduled batch processing that runs once per day.

Cloud Run Jobs are ideal for batch workloads triggered on a schedule. Using Cloud Scheduler to trigger a Cloud Run Job daily fits this pattern well.

❓ security

advanced

Securing Cloud Run Jobs with least privilege

You want to run a Cloud Run Job that accesses a Cloud Storage bucket. What is the best practice to grant access securely?

A Use the default Compute Engine service account with Owner role for the job.

B Make the Cloud Storage bucket public so the job can access it without credentials.

C Assign the Cloud Run Job's service account the Storage Object Viewer role on the bucket.

D Embed a user’s personal credentials inside the container to access the bucket.

💡 Hint

Follow the principle of least privilege for service accounts.

Granting the Cloud Run Job's service account only the Storage Object Viewer role on the specific bucket limits access to what is needed. Avoid public buckets or overly broad roles.

❓ Configuration

advanced

Configuring parallelism in Cloud Run Jobs

You want to run a Cloud Run Job with 10 parallel tasks to speed up batch processing. Which configuration achieves this?

A Set the job's max instances to 10 but keep task count at 1.

B Deploy 10 separate Cloud Run services each running one task.

C Configure the job to run a single task with 10 threads inside the container.

D Set the job's task count to 10 and max parallelism to 10 in the job spec.

💡 Hint

Cloud Run Jobs support task count and parallelism settings.

Setting the task count and max parallelism to 10 allows the job to run 10 tasks in parallel, speeding up batch processing.

✅ Best Practice

expert

Handling retries and failures in Cloud Run Jobs

You run a Cloud Run Job that processes data batches. Sometimes tasks fail due to transient errors. What is the best practice to handle retries?

A Implement retry logic inside the container code and configure the job to not retry failed tasks.

B Configure Cloud Run Job to automatically retry failed tasks with exponential backoff.

C Manually rerun the entire job when any task fails.

D Ignore failures and rely on logs to manually check for errors.

💡 Hint

Cloud Run Jobs do not have built-in automatic retries for tasks.

Cloud Run Jobs do not automatically retry failed tasks. The best practice is to implement retry logic inside the container code to handle transient errors gracefully.