GCP-ADP Data Pipeline Orchestration — Timed 10 Questions

Question 1

You need to run a task in Cloud Composer only if the previous task failed. Which Trigger Rule should you use?

Accepted Answer

one_failed. The one_failed trigger rule fires a task when at least one upstream task has failed, which is exactly the requirement to run a task only if a previous task failed. This is commonly used for error-handling or cleanup tasks that should execute on failure. It is the correct trigger rule for failure-conditional execution.

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none_failed

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always

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all_success

Question 2

You have a Workflow that must wait for a specific time duration before proceeding. Which step type is most appropriate?

Accepted Answer

sys.sleep. The sys.sleep step type is most appropriate for a Workflow that must wait for a specific time duration before proceeding. In Google Cloud Workflows, sys.sleep is a built-in function that pauses the execution for a specified duration, allowing the workflow to wait before continuing. It is designed exactly for this purpose and is simple to use.

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pause_execution

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wait_for_condition

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delay_step

Question 3

You are migrating a legacy Airflow environment to Cloud Composer. You need to ensure that local Python packages used by custom operators are installed correctly. What is the recommended approach?

Accepted Answer

Upload the requirements.txt file to the environment bucket.. The recommended approach for installing local Python packages in Cloud Composer is to upload a requirements.txt file to the environment's bucket (specifically in the dags/ folder or configured path). Composer automatically installs these dependencies on the Airflow workers and schedulers during environment creation or update.

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Create a custom Docker image for the Airflow environment.

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Manually SSH into worker nodes and run 'pip install'.

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Pre-install packages in the DAG file using 'os.system'.

Question 4

A task in your Airflow DAG involves a long-running process that might exceed the default Airflow worker pod timeout. How should you handle this?

Accepted Answer

Set the 'execution_timeout' parameter on the task instance.. Airflow's execution_timeout parameter defines the maximum runtime allowed for a task instance before the scheduler marks it as failed and terminates it. Setting this parameter on the specific long-running task overrides the default and prevents the worker from killing the task prematurely. This is the correct, task-scoped mechanism for handling processes that legitimately need more time than the default allows.

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Move the task to a custom node pool in GKE.

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Increase the 'default_dag_run_timeout'.

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Decrease the number of parallel tasks.

Question 5

You need to pass a JSON object into a Workflows execution. How should you format the argument in the 'gcloud workflows executions' command?

Accepted Answer

Use the --data flag with a JSON string.. The gcloud workflows executions command accepts runtime arguments via the --data flag, which takes a JSON string representing the input to the workflow. This is the documented, standard way to pass structured data into a Workflows execution. The workflow then accesses this data through its input argument.

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Pipe the JSON into the stdin of the command.

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Use the --env-vars flag.

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Upload the JSON to a GCS bucket and point to it.

Question 6

In Cloud Composer, you want to prevent a task from retrying if it fails due to a specific error (e.g., File Not Found). How can you configure this?

Accepted Answer

Raise 'AirflowSkipException' in your task code.. Airflow does not support per-error-type retry suppression natively, so you must raise an 'AirflowSkipException' or similar in custom code to avoid retries.

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Set 'retries: 0' in the task operator.

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Use the 'on_failure_callback' to cancel retries.

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Configure 'retry_delay' to be infinite.

Question 7

Which THREE of the following are components of a Workflows definition?

Accepted Answer

main. In a Workflows definition, the main component (A) is the required entrypoint that declares the workflow's top-level logic and orchestrates execution, making it a core part of the definition. The steps component (D) defines the individual units of work that the workflow executes, so it is also a fundamental part of the definition. The variables component (C) declares workflow-level variables that can be referenced throughout the workflow, which is likewise a standard part of a Workflows definition. By contrast, dag_schedule (B) is not a Workflows definition component but rather a scheduling construct used by other orchestration systems, and worker_config (E) is not part of a Workflows definition but relates to runtime or worker configuration outside the definition itself.

Answer

dag_schedule

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worker_config

Question 8

Which TWO of the following services can trigger Workflows executions?

Accepted Answer

Cloud Scheduler. Cloud Scheduler (D) is correct because it can invoke Workflows executions directly via an HTTP target, using an OAuth token or OIDC service account to authenticate against the Workflows API, making it the standard way to run workflows on a cron schedule. Eventarc (E) is correct because it delivers events from Google Cloud sources (and custom sources) to Workflows as a destination, triggering executions in response to those events. Cloud Functions (A) can call the Workflows API, but it is not a native trigger service for Workflows in the same way; Cloud Logging (B) is a logging and routing service, not an execution trigger; and Cloud Build (C) is a CI/CD service that builds and deploys but does not natively trigger Workflows executions.

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Cloud Functions

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Cloud Logging

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Cloud Build

Question 9

Which TWO of the following are common reasons a Workflows execution might stay in a 'PENDING' or 'FAILED' state?

Accepted Answer

Invalid YAML syntax. Option A (Invalid YAML syntax) is correct because a Workflows execution cannot be parsed or started if the workflow definition contains malformed YAML, leaving the execution stuck in a PENDING state or immediately transitioning to FAILED before any steps run. Option C (Insufficient IAM permissions) is correct because the Workflows service account must have the required IAM roles (e.g., roles/workflows.invoker, roles/logging.logWriter, or permissions to call target services); missing permissions cause the execution to fail when it attempts to invoke those resources. Option B (Airflow scheduler failure) is not applicable because Airflow is a separate orchestration product and is not part of the Workflows execution path. Option D (Database locks) is not a typical cause since Workflows does not rely on a user-managed database for execution state. Option E (Worker node exhaustion) is not relevant because Workflows is a fully managed, serverless service and does not expose or depend on worker nodes.

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Airflow scheduler failure

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Database locks

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Worker node exhaustion

Question 10

Which THREE of the following Cloud Composer 2 features provide improved performance over Composer 1?

Accepted Answer

Unified environment management. Option A (Unified environment management) is correct because Composer 2 consolidates the control plane and worker infrastructure into a single managed environment, letting you manage the whole deployment through one interface rather than juggling separate GKE and Airflow components as in Composer 1. Option D (Configurable resource allocation) is correct because Composer 2 lets you tune CPU, memory, and storage for schedulers, workers, and web servers independently, so workloads get right-sized resources for better throughput. Option E (Environment auto-scaling) is correct because Composer 2 supports autoscaling of worker replicas based on queue demand, improving performance under variable loads compared with Composer 1's fixed worker pools. Option B is incorrect because Composer 2 reduces, not requires, manual GKE cluster management. Option C is incorrect because Composer 2 uses consumption-based pricing rather than a fixed pricing model.

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Requires manual GKE cluster management

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Fixed pricing model