A retail organization wants to share standardized daily inventory and sales metrics from BigQuery with hundreds of external vendor partners. The organization has established the following operational and governance requirements: Data consumers must be able to query the shared datasets in BigQuery without duplicating storage across consumer projects (zero-copy architecture). The organization must maintain centralized governance, monitor subscriber access, and easily revoke access if commercial agreements end. Data consumers must pay for their own query compute costs without incurring egress fees on the publisher. Which Google Cloud sharing mechanism should the organization implement?

Create a data exchange in Analytics Hub, publish the BigQuery datasets as listings, and manage subscriptions for external partners.: What Analytics Hub is: Analytics Hub is a fully managed data exchange service built on top of BigQuery that allows organizations to securely discover, share, and govern datasets at scale across organizational and project boundaries. How it meets the requirements: Zero-copy architecture (✓): Analytics Hub enables data publishers to create listings backed by BigQuery datasets. Subscribers create a read-only linked dataset in their own BigQuery projects, allowing them to query the data in place without copying or duplicating underlying storage. Centralized governance and monitoring (✓): Publishers manage access through private or public exchanges, monitor subscribers, view telemetry on dataset usage, and revoke access instantly when contracts expire. Separation of storage and compute billing (✓): Subscribers pay for their own query compute slots/on-demand processing within their own GCP billing accounts, while the publisher only pays for their primary dataset storage without incurring cross-project egress fees. Key advantages and capabilities: Linked Datasets: Subscribers access live, up-to-date data immediately without needing scheduled export pipelines or ETL jobs. Granular Access Control: Exchange administrators can restrict listings to specific external organizations, Google Groups, or IAM identities. Data Clean Rooms: Supports privacy-preserving collaborative queries without exposing underlying raw or sensitive personal data. Auditability and Metrics: Integration with Cloud Audit Logs and BigQuery administration tools provides visibility into listing subscriptions. Why this is optimal: Analytics Hub is purpose-built for multi-organization data sharing and data democratization. It eliminates the maintenance burden, synchronization latency, and storage replication costs associated with traditional file-based exports or direct table replication.

Associate Data Practitioner

Unlock the power of your data in the cloud! Get hands-on with Google Cloud's core data services like BigQuery and Looker to validate your practical skills in data ingestion, analysis, and management, and earn your Associate Data Practitioner certification!

Fundamental

Prepare and test your skills

Fundamental

Prepare and test your skills

Zero-Copy Sharing with Analytics Hub Listings

Section: Determine when to share data using Analytics Hub

...

Explanations

...

View source documentation

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An enterprise analytics team manages batch Spark workloads for multiple internal business units. The workloads have the following operational requirements and constraints: Each business unit requires strict resource isolation, distinct package dependencies, and precise cost attribution. The batch jobs run intermittently or once daily on varying schedules rather than continuously. Compute costs must be minimized by eliminating idle cluster resources between scheduled executions. Persistent data must remain decoupled from the compute resources to avoid data loss. Which architecture and orchestration strategy should the team implement?

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Last updated August 29, 2026

Associate Data Practitioner

Unlock the power of your data in the cloud! Get hands-on with Google Cloud's core data services like BigQuery and Looker to validate your practical skills in data ingestion, analysis, and management, and earn your Associate Data Practitioner certification!

Fundamental

Prepare and test your skills

Fundamental

Prepare and test your skills

Zero-Copy Sharing with Analytics Hub Listings

Section: Determine when to share data using Analytics Hub

A retail organization wants to share standardized daily inventory and sales metrics from BigQuery with hundreds of external vendor partners. The organization has established the following operational and governance requirements:

Data consumers must be able to query the shared datasets in BigQuery without duplicating storage across consumer projects (zero-copy architecture).
The organization must maintain centralized governance, monitor subscriber access, and easily revoke access if commercial agreements end.
Data consumers must pay for their own query compute costs without incurring egress fees on the publisher.

Which Google Cloud sharing mechanism should the organization implement?

...

Configure automated Cloud Storage transfer jobs to export BigQuery table data to CSV files in a shared Cloud Storage bucket.

...

Create a data exchange in Analytics Hub, publish the BigQuery datasets as listings, and manage subscriptions for external partners.

...

Use Database Migration Service to continuously replicate the BigQuery datasets to a Cloud SQL instance shared with external partners.

...

Grant the BigQuery Data Viewer IAM role to individual external user accounts directly on the publisher's BigQuery dataset.

...

Explanations

What Analytics Hub is:

Analytics Hub is a fully managed data exchange service built on top of BigQuery that allows organizations to securely discover, share, and govern datasets at scale across organizational and project boundaries.

How it meets the requirements:

Zero-copy architecture (✓): Analytics Hub enables data publishers to create listings backed by BigQuery datasets. Subscribers create a read-only linked dataset in their own BigQuery projects, allowing them to query the data in place without copying or duplicating underlying storage.
Centralized governance and monitoring (✓): Publishers manage access through private or public exchanges, monitor subscribers, view telemetry on dataset usage, and revoke access instantly when contracts expire.
Separation of storage and compute billing (✓): Subscribers pay for their own query compute slots/on-demand processing within their own GCP billing accounts, while the publisher only pays for their primary dataset storage without incurring cross-project egress fees.

Key advantages and capabilities:

Linked Datasets: Subscribers access live, up-to-date data immediately without needing scheduled export pipelines or ETL jobs.
Granular Access Control: Exchange administrators can restrict listings to specific external organizations, Google Groups, or IAM identities.
Data Clean Rooms: Supports privacy-preserving collaborative queries without exposing underlying raw or sensitive personal data.
Auditability and Metrics: Integration with Cloud Audit Logs and BigQuery administration tools provides visibility into listing subscriptions.

Why this is optimal:

Analytics Hub is purpose-built for multi-organization data sharing and data democratization. It eliminates the maintenance burden, synchronization latency, and storage replication costs associated with traditional file-based exports or direct table replication.

...

View source documentation

Keep the momentum going with these hand-picked practice scenarios

FundamentalAssociate Data Practitioner

Analytics Hub Linked Dataset

Determine when to share data using Analytics Hub

FundamentalAssociate Data Practitioner

Analytics Hub

Determine when to share data using Analytics Hub

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Organize models in Model Registry

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GROUP BY Clause Requirement for Aggregation

Define and execute SQL queries in BigQuery to generate reports and extract key insights

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BigQuery Partition Expiration

Configure rules to delete objects after a specified period to automatically remove unnecessary data and reduce storage expenses (e.g., BigQuery, Cloud Storage)

AdvancedProfessional Cloud Data Engineer

An enterprise analytics team manages batch Spark workloads for multiple internal business units. The workloads have the following operational requirements and constraints: Each business unit requires strict resource isolation, distinct package dependencies, and precise cost attribution. The batch jobs run intermittently or once daily on varying schedules rather than continuously. Compute costs must be minimized by eliminating idle cluster resources between scheduled executions. Persistent data must remain decoupled from the compute resources to avoid data loss. Which architecture and orchestration strategy should the team implement?

Deciding between persistent or job-based data clusters (e.g., Dataproc)

Want more questions like this?

Get a free certification question every week.

Last updated August 29, 2026

A retail organization wants to share standardized daily inventory and sales metrics from BigQuery with hundreds of external vendor partners. The organization has established the following operational and governance requirements:

Data consumers must be able to query the shared datasets in BigQuery without duplicating storage across consumer projects (zero-copy architecture).
The organization must maintain centralized governance, monitor subscriber access, and easily revoke access if commercial agreements end.
Data consumers must pay for their own query compute costs without incurring egress fees on the publisher.

Which Google Cloud sharing mechanism should the organization implement?

Configure automated Cloud Storage transfer jobs to export BigQuery table data to CSV files in a shared Cloud Storage bucket.

Create a data exchange in Analytics Hub, publish the BigQuery datasets as listings, and manage subscriptions for external partners.

Use Database Migration Service to continuously replicate the BigQuery datasets to a Cloud SQL instance shared with external partners.

Grant the BigQuery Data Viewer IAM role to individual external user accounts directly on the publisher's BigQuery dataset.

Configure automated Cloud Storage transfer jobs to export BigQuery table data to CSV files in a shared Cloud Storage bucket.

Create a data exchange in Analytics Hub, publish the BigQuery datasets as listings, and manage subscriptions for external partners.

What Analytics Hub is:

Analytics Hub is a fully managed data exchange service built on top of BigQuery that allows organizations to securely discover, share, and govern datasets at scale across organizational and project boundaries.

How it meets the requirements:

Zero-copy architecture (✓): Analytics Hub enables data publishers to create listings backed by BigQuery datasets. Subscribers create a read-only linked dataset in their own BigQuery projects, allowing them to query the data in place without copying or duplicating underlying storage.
Centralized governance and monitoring (✓): Publishers manage access through private or public exchanges, monitor subscribers, view telemetry on dataset usage, and revoke access instantly when contracts expire.
Separation of storage and compute billing (✓): Subscribers pay for their own query compute slots/on-demand processing within their own GCP billing accounts, while the publisher only pays for their primary dataset storage without incurring cross-project egress fees.

Key advantages and capabilities:

Linked Datasets: Subscribers access live, up-to-date data immediately without needing scheduled export pipelines or ETL jobs.
Granular Access Control: Exchange administrators can restrict listings to specific external organizations, Google Groups, or IAM identities.
Data Clean Rooms: Supports privacy-preserving collaborative queries without exposing underlying raw or sensitive personal data.
Auditability and Metrics: Integration with Cloud Audit Logs and BigQuery administration tools provides visibility into listing subscriptions.

Why this is optimal:

Analytics Hub is purpose-built for multi-organization data sharing and data democratization. It eliminates the maintenance burden, synchronization latency, and storage replication costs associated with traditional file-based exports or direct table replication.

Use Database Migration Service to continuously replicate the BigQuery datasets to a Cloud SQL instance shared with external partners.

Grant the BigQuery Data Viewer IAM role to individual external user accounts directly on the publisher's BigQuery dataset.

Associate Data Practitioner

Zero-Copy Sharing with Analytics Hub Listings

Related questions

Analytics Hub Linked Dataset

Analytics Hub

Model Registry Default Alias Reassignment Requirement

GROUP BY Clause Requirement for Aggregation

BigQuery Partition Expiration

Associate Data Practitioner

Zero-Copy Sharing with Analytics Hub Listings

What Analytics Hub is:

How it meets the requirements:

Key advantages and capabilities:

Why this is optimal:

Related questions

Analytics Hub Linked Dataset

Analytics Hub

Model Registry Default Alias Reassignment Requirement

GROUP BY Clause Requirement for Aggregation

BigQuery Partition Expiration

What Analytics Hub is:

How it meets the requirements:

Key advantages and capabilities:

Why this is optimal: