GCP-ADP Data Pipeline Orchestration Practice Test 4 — 15 Questions

Question 1

In Cloud Composer, what is the default behavior if a DAG execution takes longer than the 'dagrun_timeout' setting?

Accepted Answer

The DAG run is marked as failed.. In Cloud Composer (Apache Airflow), if a DAG run exceeds the dagrun_timeout setting, the scheduler marks the DAG run as failed. This prevents stuck runs from consuming resources indefinitely and allows alerting or retry logic to kick in.

Answer

The DAG enters a 'running' state indefinitely.

Answer

The DAG run is paused.

Answer

The scheduler automatically restarts the DAG.

Question 2

Which property in a Workflows YAML definition is required to define the entry point of the workflow?

Accepted Answer

main. In Google Cloud Workflows YAML definitions, the 'main' property defines the entry point of the workflow — the first step that executes when the workflow runs. It is a required top-level field that names the starting step, and all other steps are reached via transitions from it.

Answer

entry_point

Answer

init

Answer

start

Question 3

Which command-line tool do you primarily use to deploy and manage Workflows?

Accepted Answer

gcloud. The gcloud CLI is the primary tool for deploying and managing Workflows, using commands like gcloud workflows deploy, gcloud workflows execute, and gcloud workflows describe. It authenticates via gcloud auth and integrates with all Workflows management APIs. This is the documented and supported interface for lifecycle operations.

Answer

gsutil

Answer

kubectl

Answer

bq

Question 4

You are using Airflow with a Cloud Composer environment. You want to ensure that a specific task runs on a specific day of the week. How do you configure the schedule?

Accepted Answer

Set 'schedule_interval' using cron syntax.. In Airflow (and thus Cloud Composer), the schedule of a DAG is controlled by the 'schedule_interval' parameter, which accepts cron expressions, timedelta objects, or presets like '@daily'. To run a task on a specific day of the week, you use a cron expression such as '0 0 * * 1' for every Monday. This is the standard, declarative way to define weekly schedules in Airflow.

Answer

Create a separate DAG for each day of the week.

Answer

Enable the 'weekday_only' flag in the DAG configuration.

Answer

Use the 'timedelta' object with a 7-day offset.

Question 5

You want to dynamically generate tasks in an Airflow DAG based on the number of files in a GCS bucket. What is the standard way to do this?

Accepted Answer

Use a Python loop inside the DAG file to instantiate tasks.. The standard way to dynamically generate tasks in Airflow based on external conditions (like the number of files in a GCS bucket) is to use a Python loop inside the DAG file. At DAG parse time, the loop runs and creates task instances dynamically based on the current state (e.g., listing files via a GCS hook). This leverages Airflow's core design: DAGs are Python scripts that are executed to build the task graph. The loop can call an operator (e.g., PythonOperator, GCSToBigQueryOperator) for each file, generating tasks with unique task_ids. This pattern is widely documented and recommended for dynamic task generation, as long as the number of tasks is not excessively large and the DAG is idempotent.

Answer

Use the 'SubDagOperator'.

Answer

Configure an external metadata service to inject tasks.

Answer

Use the 'DynamicTaskGroup' operator in the Airflow library.

Question 6

A Cloud Composer environment is experiencing high latency in the Airflow UI. What is a common cause and mitigation for this?

Accepted Answer

Excessive DAG complexity; optimize/simplify DAG code.. Cloud Composer's Airflow UI (webserver) performance degrades when the scheduler and webserver must parse and render an excessive number of complex DAGs, tasks, and dependencies. Overly complex DAG code — deep task graphs, heavy top-level Python, dynamic task generation — increases parsing time and metadata DB load, which manifests as UI latency. Simplifying and optimizing DAG code (reducing task count, avoiding top-level DB calls, using static DAGs) is the standard mitigation.

Answer

GKE node power failure; restart cluster.

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Too many users logged in; force logout.

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Browser cache; clear your local cache.

Question 7

Which TWO of the following are valid ways to trigger a Cloud Composer DAG?

Accepted Answer

Airflow Web UI. Option C (Airflow Web UI) is correct because Cloud Composer exposes the Airflow web server, and from the DAGs view you can manually trigger a DAG run using the 'Trigger DAG' (play) button, which creates a dag_run via the Airflow metadata database. Option E (Airflow CLI) is correct because Cloud Composer supports running Airflow CLI commands such as 'gcloud composer environments run ENVIRONMENT --location LOCATION dags trigger -- DAG_ID', which invokes the same scheduler-side trigger mechanism. Option A is wrong because the Airflow scheduler does not receive triggers by email; it only schedules DAG runs based on the DAG's schedule and metadata. Option B is wrong because the Cloud Composer console environment list does not provide a 'Run' button to trigger a DAG; it only manages environments. Option D is wrong because editing the DAG file in the bucket merely updates the DAG definition and does not itself trigger a run.

Answer

Sending an email to the Airflow scheduler.

Answer

Clicking 'Run' in the Cloud Composer console environment list.

Answer

Directly editing the DAG file in the bucket.

Question 8

What is the primary function of the Airflow 'Executor' in Cloud Composer?

Accepted Answer

Deciding how to distribute tasks to workers.. The Executor in Airflow determines how tasks are distributed and run — it decides whether tasks execute locally, on Celery workers, on Kubernetes pods, or via other backends. In Cloud Composer, the executor configuration (CeleryExecutor or KubernetesExecutor) directly controls task distribution across the worker pool. This is the executor's core responsibility in the Airflow architecture.

Answer

Serving the Airflow UI web pages.

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Storing the DAG file metadata.

Answer

Automatically scaling the GKE cluster.

Question 9

Which TWO of the following are best practices for managing Cloud Composer DAG dependencies?

Accepted Answer

Use plugins for shared code. Option A is correct because Cloud Composer plugins let you package and reuse shared code (operators, hooks, sensors, and libraries) in a versioned, centrally managed way, avoiding duplication and drift across many DAGs. Option E is correct because keeping DAG files small and focused reduces parse time in the Composer scheduler, limits the blast radius of changes, and makes dependencies between DAGs easier to reason about and maintain. Option B is wrong because DAGs are Python files that belong in the Composer environment's dags/ folder (or a synced GCS bucket), not in a database. Option C is wrong because hardcoding configurations is an anti-pattern; you should use Airflow Variables, Connections, environment variables, or secrets managers. Option D is wrong because cramming all tasks into a single DAG file creates a monolithic, hard-to-maintain DAG and does not reflect best practice for managing dependencies.

Answer

Store DAGs in a database

Answer

Hardcode all configurations

Answer

Run all tasks in a single DAG file

Question 10

Which THREE of the following are key benefits of using Cloud Composer for data orchestration?

Accepted Answer

Uses standard open-source Airflow. Option A is correct because Cloud Composer is built on the open-source Apache Airflow project, so users can author DAGs with standard Airflow operators, hooks, and Python APIs without vendor-specific rewrites. Option D is correct because Cloud Composer is a fully managed service: Google provisions and maintains the Airflow environment, including the scheduler, web server, workers, and database, plus upgrades and patching. Option E is correct because Cloud Composer provides native integrations with GCP services such as BigQuery, Cloud Storage, Dataflow, Dataproc, and Pub/Sub through built-in operators and connections. Option B is not a key benefit of Composer itself, since transformation is performed by the tasks/services the DAGs invoke rather than by the orchestration layer. Option C is incorrect because Cloud Composer is not free for all users; it is a paid service billed for the environment's underlying compute, storage, and networking resources.

Answer

Automatic data transformation

Answer

Free tier for all users

Question 11

Which THREE of the following are valid Airflow connection types in Cloud Composer?

Accepted Answer

HTTP. Option A (HTTP) is correct because Cloud Composer's Airflow connection types include 'HTTP', which stores host, schema, login, password, and port for calling REST endpoints via the HttpHook. Option D (Google Cloud) is correct because 'Google Cloud' is a built-in connection type used to store a GCP service account key or scopes, enabling operators like BigQueryInsertJobOperator and GCS hooks to authenticate. Option E (Postgres) is correct because 'Postgres' is a standard Airflow connection type that stores host, schema, login, password, and port for the PostgresHook and PostgresOperator. Option B (Kubernetes Pod) is not a connection type; KubernetesPodOperator uses a 'Kubernetes' connection (or in-cluster config), not 'Kubernetes Pod'. Option C (Python) is not a connection type; Python callables are executed via the PythonOperator, and no 'Python' connection exists in Airflow's connection type list.

Answer

Kubernetes Pod

Answer

Python

Question 12

Which TWO of the following strategies help reduce Cloud Composer costs?

Accepted Answer

Delete unused environments. Option C (Delete unused environments) is correct because Cloud Composer charges for the underlying GKE nodes, Cloud SQL database, and web server that run continuously per environment, so removing environments that are no longer needed eliminates those recurring infrastructure costs. Option E (Enable auto-scaling) is correct because Composer's autoscaling dynamically adjusts the number of worker nodes based on the workload's queue demand, allowing the environment to scale down during idle periods and avoid paying for excess capacity. Option A (Keep all historical logs forever) is wrong because retaining logs indefinitely increases Cloud Logging storage costs rather than reducing them. Option B (Use the largest environment size) is wrong because larger environments provision more workers and thus cost more, not less. Option D (Increase scheduler frequency) is wrong because more frequent scheduler runs increase resource consumption and can raise costs instead of lowering them.

Answer

Keep all historical logs forever

Answer

Use the largest environment size

Answer

Increase scheduler frequency

Question 13

Which THREE of the following are common steps in a typical data pipeline orchestration pattern?

Accepted Answer

Validation. In a typical data pipeline orchestration pattern, data ingestion (C) is a core step because it moves raw data from sources such as databases, APIs, or files into the pipeline for processing. Transformation (D) is also essential, as it cleanses, enriches, aggregates, or otherwise converts ingested data into a usable format for analytics or downstream systems. Validation (A) is a common pipeline step that checks data for schema conformance, completeness, quality, and business rules before or after transformation to prevent bad data from propagating. Network cable installation (B) and hardware provisioning (E) are infrastructure or physical-layer tasks, not orchestration steps within a data pipeline workflow, so they are not correct.

Answer

Network cable installation

Answer

Hardware provisioning

Question 14

Which TWO of the following are true about Workflows' serverless nature?

Accepted Answer

Scales automatically. Option B is correct because Workflows is a fully managed serverless orchestration service that automatically scales its execution capacity with demand, so you never provision or size workers yourself. Option C is correct because, as a serverless offering, Google manages all underlying infrastructure — servers, scaling, and availability — leaving you with no infrastructure to manage. Option A is wrong because Workflows does not require a dedicated GKE cluster; it runs on Google-managed infrastructure and can call GKE-hosted services but does not depend on a cluster. Option D is wrong because Workflows is not always free — it is billed per step executed, with a free tier only up to certain monthly limits. Option E is wrong because patching is handled by Google, not manually by the user, which is a core benefit of the serverless model.

Answer

Requires a dedicated GKE cluster

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Is always free

Answer

Requires constant manual patching

Question 15

Which THREE of the following are valid methods for troubleshooting failed tasks in Cloud Composer?

Accepted Answer

Checking the Airflow web UI task state. Option A is correct because the Airflow web UI shows each task instance's state (queued, running, success, failed, up_for_retry, etc.), letting you quickly identify which tasks failed and drill into their details. Option D is correct because the Composer scheduler logs record DAG parsing errors, scheduling decisions, and task-queueing problems, which are essential for diagnosing why tasks never ran or were stuck. Option E is correct because task instance logs capture the actual stdout/stderr from the task's execution, revealing the specific exception or failure cause. Option B is not appropriate because rebuilding the entire GKE cluster is a drastic, disruptive action that destroys the environment rather than a troubleshooting step. Option C is not appropriate because deleting the environment bucket removes DAGs, logs, and data, causing data loss and breaking the environment instead of diagnosing the failure.

Answer

Rebuilding the entire GKE cluster

Answer

Deleting the environment bucket