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Professional Cloud Data Engineer
Publishing datasets
A retail enterprise wants to establish a decentralized data-…

Professional Cloud Data Engineer

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Practice Test

Dataplex Catalog Analytics Hub Cost Sharing

Section: Publishing datasets

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Related questions

Keep the momentum going with these hand-picked practice scenarios

AdvancedProfessional Cloud Data Engineer

Your enterprise maintains a central BigQuery dataset named source_sales in project corp-analytics-prod that contains detailed order information and customer personally identifiable information (PII). You need to publish a collection of curated analytical views that exclude sensitive PII to an external analytics partner group (partner-analysts@partner.com). The implementation must satisfy the following requirements: Prevent partner analysts from querying or browsing underlying tables in source_sales directly. Automatically permit all current and future views in the publishing dataset to read data from source_sales without individual view authorization. Follow the principle of least privilege for IAM permissions while allowing partner analysts to execute queries against the published views. Which configuration should you implement?

Publishing datasets

AdvancedProfessional Cloud Data Engineer

An enterprise publishes shared data listings using BigQuery Analytics Hub. A subscriber team in caller project analytics-subscriber (Project R) has subscribed to a listing and provisioned a linked dataset in project subscriber-warehouse (Project L). The underlying shared dataset contains views in project subscriber-warehouse that reference underlying base tables hosted in project source-storage (Project V). Each project resides in a separate, isolated VPC Service Controls perimeter. Queries executed by analysts in Project R against the linked dataset views are currently failing due to perimeter violations. Which VPC Service Controls ingress and egress rules must you configure to allow users in Project R to query the views while maintaining perimeter security across all projects?

Publishing datasets

AdvancedProfessional Cloud Data Engineer

An enterprise data engineering team is designing a cost-optimized storage architecture on Cloud Storage for large-scale analytical training datasets and compliance logs. The data lifecycle and operational requirements are as follows: Ingestion & Active Processing (Days 1–30): Newly ingested datasets are actively read, transformed, and queried multiple times per day by data pipelines. Periodic Validation (Days 31–90): Datasets are accessed at most once per month for model validation and intermittent quarterly reporting. Long-term Compliance (Day 91 to 7 Years): Data must be retained for regulatory compliance for seven years, accessed less than once a year, but any ad-hoc audit queries require sub-second (millisecond) retrieval latency. Cost Governance: The strategy must minimize total storage costs while strictly avoiding early deletion fees and premature class transition penalties. Which Object Lifecycle Management policy should the team configure to meet these requirements?

Planning for storage costs and performance

AdvancedProfessional Cloud Data Engineer

An enterprise runs two primary BigQuery workloads under a capacity-based reservations model: Interactive BI Dashboards: Used concurrently by analysts and business users throughout the day, requiring consistent low-latency response times without queuing delays. Batch ETL Pipelines: Executed periodically throughout the day and night to process large staging tables; several queries contain complex Common Table Expressions (CTEs) referenced multiple times across downstream calculations. Recent monitoring shows that interactive dashboard queries experience sudden latency spikes when batch ETL tasks trigger rapid slot autoscaling. Furthermore, short-lived ETL queries that finish in a few seconds are driving unnecessary compute expenses by causing the autoscaler to provision large slot spikes, and multi-referenced CTEs are consuming excessive processing resources. Which strategy should you implement to optimize performance, eliminate workload contention, and control costs?

Interactive or batch query jobs

AdvancedProfessional Cloud Data Engineer

Your enterprise runs mission-critical hourly data transformation pipelines and executive dashboards in a dedicated Production folder, while data scientists run unpredictable, heavy ad-hoc queries in an Analytics folder. During peak hours, ad-hoc queries consume excessive compute resources, causing slot contention and delayed execution for production pipelines. You need to implement a capacity-based workload management architecture in BigQuery that: Guarantees dedicated, predictable processing capacity for production workloads with the ability to dynamically burst during unexpected demand spikes. Fully isolates production capacity so idle slots are never shared with or preempted by ad-hoc analytics jobs. Allows ad-hoc analytics workloads to scale dynamically up to a defined ceiling without impacting the production workloads. Minimizes administrative overhead by managing assignments hierarchically. Which configuration should you implement?

Ensuring that enough resources are available for business-critical data processes

AdvancedProfessional Cloud Security Engineer

An enterprise organization stores database credentials in Google Cloud Secret Manager. The security team must implement continuous credential hygiene for these database secrets according to the following requirements: The credentials must be automatically rotated on a recurring 30-day schedule without microservice downtime. Secret Manager must emit events to trigger an automated rotation handler service when a rotation is due. The rotation service must generate a new credential, apply it to the database, add a new secret version to Secret Manager, validate application health, and disable the superseded secret version. The architecture must follow Google Cloud least-privilege security best practices for service integration. Which workflow and architectural configuration should the security engineer implement?

Securing secrets with Secret Manager.

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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.

A retail enterprise wants to establish a decentralized data-sharing architecture to publish curated demand-forecasting datasets and file assets to hundreds of vendor partners and internal domain teams. The data architecture team needs to implement a solution that fulfills the following operational and financial requirements:

Provide centralized discoverability and rich metadata annotations across all published SQL datasets and storage filesets.
Enforce a cost-sharing model where the publishing team pays only for underlying data storage, while consumers incur the compute and query execution costs.
Eliminate unnecessary data replication and maintenance overhead across consumer projects.

Which combination of Google Cloud services and configurations should the enterprise implement?

Implement daily Dataflow batch pipelines to export and copy published tables and Cloud Storage filesets directly into each subscribing consumer project.

Catalog the assets using Dataplex Universal Catalog, publish the datasets using BigQuery sharing listings for consumers to query via linked datasets, and configure Requester Pays on shared Cloud Storage buckets.

Publish datasets as BigLake external tables over Cloud Storage, granting consumers Storage Object Viewer access to the underlying bucket without configuring data exchanges.

Create authorized views in the publisher BigQuery project, provision a dedicated flat-rate slot reservation assigned to those views, and grant IAM Data Viewer roles directly to consumer service accounts.

Implement daily Dataflow batch pipelines to export and copy published tables and Cloud Storage filesets directly into each subscribing consumer project.

Catalog the assets using Dataplex Universal Catalog, publish the datasets using BigQuery sharing listings for consumers to query via linked datasets, and configure Requester Pays on shared Cloud Storage buckets.

What the solution is

This architecture combines Dataplex Universal Catalog for centralized metadata management and asset discovery with BigQuery sharing (Analytics Hub) listings and Requester Pays Cloud Storage buckets to enforce zero-copy data publishing and subscriber-funded compute.

How it meets the requirements

Discoverability & Metadata: Dataplex Universal Catalog automatically indexes technical schemas for BigQuery tables and curated data lake filesets while allowing publishers to attach business glossaries, tags, and documentation URLs.
Cost-Sharing Model: In BigQuery sharing, the publisher pays solely for the persistent storage of the data. Subscribers pay for all compute resources consumed when running queries against their local linked datasets (using on-demand or capacity-based reservations). Similarly, enabling Requester Pays on Cloud Storage buckets ensures that API and egress network charges are billed to the requesting consumer project rather than the publisher.
Eliminating Replication: BigQuery sharing establishes zero-copy linked datasets in subscriber projects that serve as live read-only pointers to the source data, preventing data drift and redundant storage costs.

Key advantages

Zero-Copy Architecture: Allows hundreds of external and internal consumers to access real-time data without pipeline orchestration or data synchronization.
Granular Cost Attribution: Shifts variable query compute and data extraction costs directly to consumer billing accounts.
Centralized Governance: Standardizes metadata tagging, searchability, and data access policies in place without data movement.
Secure Publishing: Preserves security controls such as row-level access and column-level security across linked datasets.

Why this is optimal

This approach aligns with Google Cloud data mesh best practices by separating data storage from analytical compute, providing robust discovery, and automating cost attribution across organizational boundaries.

Publish datasets as BigLake external tables over Cloud Storage, granting consumers Storage Object Viewer access to the underlying bucket without configuring data exchanges.

Create authorized views in the publisher BigQuery project, provision a dedicated flat-rate slot reservation assigned to those views, and grant IAM Data Viewer roles directly to consumer service accounts.