An enterprise is building an enterprise data lakehouse architecture on Google Cloud using Dataplex to ingest and process transaction records from multiple operational databases. The architecture must satisfy the following requirements: Multi-tiered staging: Isolate raw, unvalidated ingestion data from validated, standardized data ready for analytics consumption. Schema discovery and evolution: Automatically register data definitions in the catalog while preventing unvalidated structural breaking changes in downstream consumption tables. Storage cost and performance optimization: Minimize query scanning overhead and transition aging raw data to lower-cost storage classes. Which Dataplex lake and storage architecture should the team implement?

Create a Dataplex lake containing a Raw zone (configured for Cloud Storage buckets with Object Lifecycle Management to transition objects to Nearline/Coldline) and a Curated zone (configured for BigLake Parquet tables partitioned by ingestion date). Enable Dataplex metadata harvesting with schema discovery on the Raw zone, while enforcing strict schema definitions and automated data quality checks before promoting transformed data into the Curated zone.: ### What this architecture is This architecture establishes a multi-tiered **Dataplex lake** topology dividing storage assets into distinct **Raw** and **Curated zones**. It pairs Cloud Storage object lifecycle management with columnar BigLake storage formats and automated metadata discovery. ### How it meets the requirements - **Multi-tiered staging:** Dataplex organizes storage buckets and BigQuery datasets into **Raw** (unstructured/semi-structured landing) and **Curated** (standardized, cleaned, and modeled) asset zones, ensuring logical isolation and access boundary enforcement. - **Schema discovery and evolution:** Enabling automated metadata harvesting and schema discovery in the **Raw zone** allows Dataplex to detect incoming file structures without breaking downstream pipelines. Enforcing schema contracts and data quality scans before promotion protects **Curated zone** tables from unvalidated schema drift. - **Storage cost and performance optimization:** **Object Lifecycle Management** on raw Cloud Storage buckets automatically shifts objects to **Nearline** or **Coldline** storage classes after defined retention intervals. Partitioning the curated **BigLake Parquet** tables by date ensures query pruning and maximizes scan efficiency in analytical workloads. ### Key advantages - **Granular Governance:** Applies uniform IAM security policies across distributed buckets and tables within specific Dataplex zones. - **Data Integrity:** Blocks malformed or non-conforming data records at the Raw-to-Curated transformation stage. - **Query Optimization:** Partitioning and columnar Parquet format drastically reduce BigQuery bytes scanned and query execution latencies. - **Automated Lifecycle:** Automatically reduces storage costs without manual engineering intervention.

Dataplex Lakehouse Multi Tier Staging Architecture | professional-cloud-data-engineer

Dataplex Lakehouse Multi Tier Staging Architecture

Worked example. The correct answer is already marked and every option is explained below, so there is nothing to select here. To answer questions yourself, start the free trial.

An enterprise is building an enterprise data lakehouse architecture on Google Cloud using Dataplex to ingest and process transaction records from multiple operational databases. The architecture must satisfy the following requirements:

Multi-tiered staging: Isolate raw, unvalidated ingestion data from validated, standardized data ready for analytics consumption.
Schema discovery and evolution: Automatically register data definitions in the catalog while preventing unvalidated structural breaking changes in downstream consumption tables.
Storage cost and performance optimization: Minimize query scanning overhead and transition aging raw data to lower-cost storage classes.

Which Dataplex lake and storage architecture should the team implement?

Create a Dataplex lake containing a Raw zone (configured for Cloud Storage buckets with Object Lifecycle Management to transition objects to Nearline/Coldline) and a Curated zone (configured for BigLake Parquet tables partitioned by ingestion date). Enable Dataplex metadata harvesting with schema discovery on the Raw zone, while enforcing strict schema definitions and automated data quality checks before promoting transformed data into the Curated zone.

Create a single Dataplex Raw zone that contains both the Cloud Storage landing buckets and production BigQuery datasets. Configure Dataplex to automatically propagate all detected schema modifications directly to production analytics tables, and rely on Cloud SQL automatic storage scaling to manage analytical capacity.

Deploy a Dataplex Curated zone mapped exclusively to unindexed Firestore in Datastore mode entities. Implement keys-only projection queries to prevent data quality issues, and use Cloud KMS key version rotation to automate the archiving and lifecycle tiering of historical records.

Configure a single Cloud Storage bucket within a Dataplex Raw zone using unpartitioned CSV files. Route all analytical queries through Cloud Service Mesh using the waterfall by zone load balancing algorithm to minimize cross-zone query latency across Dataplex assets.

Professional Cloud Data Engineer

Dataplex Lakehouse Multi Tier Staging Architecture

Related questions