Google Google Cloud Certified Professional-Data-Engineer New Questions

The key requirements for sharing the sales dataset are:

Self-serving for other business units.

Low-maintenance.

Cost-effective.

Sharing with other business units (implying potentially different projects).

Analytics Hub (Option C) is designed precisely for this purpose of sharing data assets (like datasets) in a governed, discoverable, and self-service manner across an organization and even externally.

Self-Serving: Consumers (other business units) can browse available datasets in an exchange and subscribe to them. This makes it easy for them to discover and access the data they need without manual intervention from the data provider for each request.

Low-Maintenance for Provider: Once a dataset is published as a "listing" in Analytics Hub, the provider doesn't need to manage individual access requests or data copying for each new consumer project that subscribes. Updates to the source dataset are reflected for subscribers.

Cost-Effective:No Data Duplication for Sharing: When a dataset is shared via Analytics Hub, subscribers query the data directly from the provider's project (unless the provider explicitly opts for a replicated dataset model, which is less common for internal sharing where live access is preferred). This avoids storage costs associated with duplicating large datasets in multiple projects.

Query Costs: Query costs are typically borne by the subscriber's project.

Governed Sharing: Analytics Hub provides a centralized way to manage and audit data sharing.

Let's analyze why other options are less suitable:

A (Enable access to authorized views): Authorized views are a good way to share specific slices or aggregations of data without exposing the underlying tables. However, managing authorizations for potentially many views across many business units/projects can become less "self-serving" and more "low-maintenance" than a dedicated data exchange platform. Discoverability is also less centralized.

B (BigQuery Data Transfer Service to copy): This creates data copies, which increases storage costs and can lead to data staleness if the copy schedule isn't frequent enough. It's not "low-maintenance" as it requires managing DTS jobs and storage for copies. It's generally not the most cost-effective way to share for querying.

D (Create and share views with users): Similar to authorized views, but sharing directly with individual users can be a permissions management challenge at scale compared to project-level or group-level subscriptions facilitated by Analytics Hub. It lacks the "exchange" concept for discovery and self-service subscription by business units/projects.

The key requirements are:

On-premises PostgreSQL database.

Run transactional workloads AND support analytics needs with a single database.

Move to Google Cloud without changing database management systems (i.e., remain PostgreSQL-compatible).

Minimize cost and complexity.

AlloyDB for PostgreSQL (Option A) is the best fit for these requirements.

PostgreSQL-Compatible: AlloyDB is fully PostgreSQL-compatible, meaning minimal to no application changes are required ("without changing database management systems").

Transactional and Analytical Workloads: AlloyDB is designed to handle demanding transactional workloads while also providing significantly faster analytical query performance compared to standard PostgreSQL. It achieves this through its intelligent, database-optimized storage layer and columnar engine integration. This addresses the "single database" for both needs.

Cost and Complexity: As a managed service, it reduces operational complexity. Its performance benefits for both OLTP and OLAP can lead to better cost-efficiency by handling mixed workloads effectively on a single system.

Let's analyze why other options are less suitable:

B (Migrate to BigQuery): BigQuery is an analytical data warehouse, not designed for transactional workloads. This violates the "single database" for both types of workloads and "without changing database management systems" (as BigQuery is not PostgreSQL).

C (Migrate to Cloud Spanner): Cloud Spanner is a globally distributed, horizontally scalable relational database. While excellent for high-availability transactional workloads, it has its own SQL dialect (ANSI 2011 with extensions, not fully PostgreSQL wire-compatible without tools like PGAdapter, which adds complexity) and a different architecture. This would involve more significant changes than moving to a PostgreSQL-compatible system. The requirement was "without changing database management systems."

D (Migrate to Cloud SQL for PostgreSQL): Cloud SQL for PostgreSQL is a fully managed PostgreSQL service. It's excellent for transactional workloads and simpler analytical queries. However, for more demanding analytical needs on the same database instance, AlloyDB is specifically optimized to provide superior performance due to its architectural enhancements (like the columnar engine). If the analytical needs are significant, AlloyDB offers a better converged experience. While Cloud SQL is PostgreSQL-compatible, AlloyDB is positioned for superior performance on mixed workloads.