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Professional Cloud Data Engineer
Scheduling and orchestrating jobs in a repeatable way
An enterprise data engineering team is building an event-dri…

Professional Cloud Data Engineer

professional-cloud-data-engineer

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Description
Sample Question
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Practice Test

Serverless Orchestration Cloud Run Dataflow Workflows

Section: Scheduling and orchestrating jobs in a repeatable way

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Related questions

Keep the momentum going with these hand-picked practice scenarios

AdvancedProfessional Cloud Data Engineer

An organization runs an hourly batch data pipeline orchestrated by Cloud Scheduler that invokes an HTTP endpoint hosted on Cloud Run. The service ingests transaction data and writes the processed records into BigQuery. Due to transient network interruptions, the HTTP target occasionally receives duplicate invocations for a single scheduled run. Furthermore, long-running job attempts sometimes cause Cloud Scheduler to mark an attempt as failed even though the Cloud Run container is still processing the request, blocking subsequent hourly executions. You need to ensure idempotent execution across retry attempts and resolve the job execution timeout mismatch. What should you do?

Scheduling and orchestrating jobs in a repeatable way

AdvancedProfessional Cloud Data Engineer

A data engineering team is architecting two interdependent workflows in Google Cloud Composer: an upstream ingestion workflow (DAG_Ingest) and a downstream transformation workflow (DAG_Analytics). The team must fulfill the following architectural and operational requirements: DAG_Analytics must begin processing only after DAG_Ingest completes and publishes an event artifact notification. Prevent automated scheduling of historical backfill runs when DAG_Analytics is uploaded or unpaused. Prevent resource starvation of core Airflow components (schedulers and web servers) caused by CPU- and memory-heavy transformation tasks. Ensure robust task failure recovery without whole-workflow failure. Which combination of Airflow configurations, operators, and task relationships should the team implement?

Scheduling and orchestrating jobs in a repeatable way

AdvancedProfessional Cloud Data Engineer

A lead data engineer is designing a data lifecycle and cost-optimization architecture for an enterprise BigQuery data warehouse. The requirements are: All newly created partitioned event tables in the telemetry_events dataset must automatically delete individual daily partitions once they are older than 60 days, while retaining the base table structure for ongoing streaming ingestion. Any non-partitioned lookup tables created in the same dataset must be preserved indefinitely by default and automatically transition to BigQuery's lower-cost long-term storage tier after remaining unmutated for 90 days. High-priority analytics tables must be able to override the default partition retention to keep partitions for 180 days upon table creation. Which configuration should the data engineer implement?

Lifecycle management of data

AdvancedProfessional Cloud Data Engineer

A data engineering team manages a daily batch pipeline orchestrated by Cloud Composer that processes terabytes of transactional log files from Cloud Storage into partitioned BigQuery tables. Recently, transient worker pod evictions and network timeouts have triggered task retries, resulting in duplicate data rows in BigQuery and failed task executions caused by exhausted worker disk space. You need to re-architect the workflow orchestration to ensure fault tolerance, prevent duplicate records during task retries (idempotency), and prevent worker resource exhaustion. Which architectural strategy should you implement?

AdvancedProfessional Cloud Data Engineer

Your organization manages a BigQuery dataset containing a customer transactions table with sensitive Personally Identifiable Information (PII), including columns for tax_id, email_address, and credit_card_number. You are preparing this data for enterprise-wide visualization and reporting. You have the following security and access requirements: BI Analysts must be able to query the table to build dashboards and perform aggregations, but must only receive real-time obfuscated (masked) values for sensitive columns. Compliance Auditors must be able to inspect and view the unmasked, cleartext values of these sensitive columns. Unauthorized Users without explicit permissions must be blocked from querying the sensitive columns entirely. The security controls must be centrally managed and applied dynamically at query runtime without maintaining duplicate tables, views, or ETL pipelines. Which solution should you implement?

Security, data masking, Identity and Access Management (IAM), and Cloud Data Loss Prevention (Cloud DLP)

AdvancedProfessional Cloud DevOps Engineer

A DevOps team manages a business-critical microservice running on Google Kubernetes Engine (GKE). The team has defined an availability Service Level Objective (SLO) of 99.9% over a rolling 30-day window. Currently, the team experiences alert fatigue caused by transient error spikes triggering paging alerts, while simultaneously missing slow, steady budget drain that exhausts the monthly error budget before scheduled reviews. Which alerting strategy should the team implement in Cloud Monitoring to reliably detect both rapid and slow budget depletion while minimizing false positives?

Configuring alerting and alerting policies (e.g., SLIs, SLOs, cost control)

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Last updated August 27, 2026

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Professional Cloud Data Engineer

Provider: Google Cloud
Passing score: 70%
Questions: 50
Duration: 90 minutes

Other sections in this exam

Designing data processing systems
Ingesting and processing the data
Storing the data
Preparing and using data for analysis
Maintaining and automating data workloads

Ask Scout

Still curious? Scout, our AI tutor, can explain this concept further and answer your follow-up questions.

An enterprise data engineering team is building an event-driven, automated data processing pipeline on Google Cloud. The workflow must execute sequentially whenever new files arrive in Cloud Storage:

Validate incoming event metadata and write an audit payload.
Execute a containerized data-cleansing task implemented as a Cloud Run job, passing dynamic input file paths and execution parameters as environment variable overrides at runtime.
Launch a downstream Dataflow batch pipeline using the outputs generated by the Cloud Run job.

The entire orchestration layer must be completely serverless, scale to zero when idle with no persistent compute infrastructure, and provide built-in retry and state-handling capabilities.

Which orchestration approach should the team implement?

Deploy an Apache Airflow environment on a self-managed Google Kubernetes Engine (GKE) cluster, and use BashOperator tasks to run gcloud CLI commands that trigger the Cloud Run job and Dataflow pipeline.

Configure a Managed Instance Group (MIG) with a custom task-farming agent that polls Pub/Sub topics to execute shell commands for Cloud Run and Dataflow.

Create a Dataproc Workflow Template with OrderedJob configurations to execute the container image and coordinate the Dataflow template submission.

Create a Cloud Workflows pipeline triggered by an Eventarc Cloud Storage event, using native connectors to invoke the Cloud Run job with container overrides and execute the Dataflow job.

Deploy an Apache Airflow environment on a self-managed Google Kubernetes Engine (GKE) cluster, and use BashOperator tasks to run gcloud CLI commands that trigger the Cloud Run job and Dataflow pipeline.

Configure a Managed Instance Group (MIG) with a custom task-farming agent that polls Pub/Sub topics to execute shell commands for Cloud Run and Dataflow.

Create a Dataproc Workflow Template with OrderedJob configurations to execute the container image and coordinate the Dataflow template submission.

Create a Cloud Workflows pipeline triggered by an Eventarc Cloud Storage event, using native connectors to invoke the Cloud Run job with container overrides and execute the Dataflow job.

What Cloud Workflows is

Cloud Workflows is a fully managed, serverless orchestration platform that coordinates Google Cloud services, external APIs, and containerized workloads into reliable, automated state machines without requiring any underlying infrastructure management.

How it meets the requirements

Event-driven execution: It integrates natively with Eventarc, enabling instant workflow execution whenever new files or Pub/Sub messages are received.
Dynamic parameter passing and container overrides: Using the built-in Cloud Run connector (googleapis.run.v1.namespaces.jobs.run), Workflows dynamically injects parameters such as input file locations and environment variables into the container via containerOverrides.
Chaining serverless services and Dataflow: It coordinates the sequential execution of logging, containerized Cloud Run jobs, and Dataflow pipelines while managing state transitions and capturing step results.
Serverless and pay-per-use: Cloud Workflows requires zero provisioned VM infrastructure, scales to zero when idle, and charges only for execution steps.

Key advantages and capabilities

Native API Connectors: Offers out-of-the-box connectors for Cloud Run, Dataflow, BigQuery, and Cloud Storage with automatic authentication and error handling.
Dynamic runtime configuration: Passes state, JSON payloads, and dynamic execution arguments between heterogeneous pipeline steps.
Configurable resilience: Provides native retry policies, exponential backoff, and exception handling directly within the YAML/JSON workflow definition.
Zero operational overhead: Eliminates cluster maintenance, worker sizing, and software patching.

Why this is optimal

Unlike heavy, dedicated orchestrators like Apache Airflow or self-managed worker pools, Cloud Workflows paired with Eventarc delivers an entirely serverless, low-latency, and cost-effective solution tailored for coordinating microservices, container jobs, and managed data pipelines.