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Professional Cloud Data Engineer
Choosing managed services (e.g., BigQuery, BigLake, AlloyDB, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore)
An enterprise financial services company is modernizing its …

Professional Cloud Data Engineer

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Sample Question
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Practice Test

Evaluate Transactional Database Scaling Consistency

Section: Choosing managed services (e.g., BigQuery, BigLake, AlloyDB, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore)

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An enterprise financial services company is modernizing its mission-critical core banking system on Google Cloud. The system has the following architectural requirements:

Support a structured relational schema with strict ACID transaction guarantees and serializable isolation.
Provide horizontal write scalability across multiple geographical regions without requiring application-level sharding.
Maintain strong external consistency with synchronous multi-region replication.
Meet a 99.999% (five-nines) availability Service Level Agreement (SLA).

Which database service should the organization select to meet these requirements?

Memorystore

Firestore

Cloud SQL

Spanner

Explanations

What Spanner is

Spanner is Google Cloud's fully managed, globally distributed relational database service. It combines the benefits of relational database structure (schemas, ANSI SQL support, ACID transactions) with the horizontal scalability of NoSQL systems.

How it meets the requirements

Structured relational schema and serializable ACID transactions: Spanner natively enforces relational schemas, ANSI SQL queries, and provides serializable isolation by default to maintain external consistency.
Multi-region horizontal write scalability: Spanner scales horizontally across compute nodes and storage without manual application sharding, handling distributed transactions automatically via TrueTime.
Strong external consistency: Spanner uses synchronous, byte-level replication across zones and regions with Paxos consensus, ensuring strict read-after-write consistency globally.
99.999% availability SLA: Spanner's multi-region instance configurations offer an industry-leading 99.999% availability SLA with automatic failover and zero planned downtime.

Key advantages

Unlimited horizontal scaling: Seamlessly scales compute and storage independently to handle massive transactional volumes.
Synchronous replication: Guarantees zero data loss (RPO = 0) across regional failures.
Familiar relational model: Supports standard SQL dialects (GoogleSQL and PostgreSQL interface) and DML.
No maintenance windows: Schema updates and maintenance occur online without service disruption.

Why this is optimal

Spanner is the only Google Cloud managed database that simultaneously provides global horizontal write scaling, multi-region synchronous replication, serializable ACID transactions, and up to 99.999% availability.

View source documentation

Related questions

Keep the momentum going with these hand-picked practice scenarios

AdvancedProfessional Cloud Data Engineer

A connected vehicle platform ingests real-time telemetry from over 500,000 vehicles streaming diagnostic metrics at 1-second intervals. The data engineering team needs to design a high-throughput storage tier in Cloud Bigtable that satisfies the following requirements: Eliminate write hotspots across cluster tablets during peak driving hours. Support low-latency row range scans to retrieve historical telemetry for a specific vehicle over a designated time window. Maintain an instance configuration optimized specifically for query latency under high write volumes. Which row key pattern and cluster target should you implement?

Choosing managed services (e.g., BigQuery, BigLake, AlloyDB, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore)

AdvancedProfessional Cloud Data Engineer

An enterprise is designing a cost-effective data lakehouse architecture on Google Cloud. Several petabytes of structured analytical data are maintained in open columnar formats (such as Parquet) across Cloud Storage buckets and external cloud object stores. The security team requires that analysts query the data using standard BigQuery SQL and authorized views with fine-grained access controls, while strictly prohibiting granting end users direct object read permissions (storage.objects.get) to the underlying storage buckets. Which architecture should you implement to meet these requirements?

AdvancedProfessional Cloud Data Engineer

An enterprise processes terabytes of analytics data daily using batch Apache Beam pipelines on Dataflow. The primary input dataset currently resides in a Cloud Storage multi-region bucket located in the us multi-region, while the pipeline's temporary and staging files (gcpTempLocation) are written to a bucket in us-east1. The Dataflow worker compute instances are provisioned to execute in us-central1. The cloud operations team reports substantial inter-region network egress charges and increased execution wall time during pipeline shuffle and staging phases. Additionally, the batch workloads are non-time-sensitive. Which architectural strategy should the data engineering team implement to minimize network egress costs, improve I/O performance, and optimize economic efficiency?

AdvancedProfessional Cloud Data Engineer

An e-commerce company is building a real-time analytics streaming pipeline in Apache Beam on Cloud Dataflow to process high-volume clickstream and transaction events from Pub/Sub. The pipeline must fulfill the following architectural requirements: Group individual user actions into user sessions that close after 15 minutes of inactivity. Emit speculative aggregations as events arrive before the session closes, and update downstream stores with updated totals whenever late-arriving events arrive within an allowed lateness threshold of 1 hour. Enrich the event stream with store location metadata from a slowly changing table without stalling the streaming execution. Which combination of Apache Beam transformations, windowing strategies, triggers, and enrichment mechanisms should you implement?

AdvancedProfessional Cloud Data Engineer

An enterprise is planning a multi-workload migration strategy to Google Cloud and must select the optimal data transfer mechanisms based on data source type, transfer volume, and network availability: Workload 1: A 350 TB historical file archive stored on an on-premises Network Attached Storage (NAS) system. The archive must be moved to Cloud Storage within two weeks, but the facility's available outbound internet bandwidth is constrained to 100 Mbps and cannot be upgraded. Workload 2: A mission-critical on-premises transactional PostgreSQL database that powers an active production system. It must be migrated to Cloud SQL for PostgreSQL with minimal downtime using continuous replication until the final cutover. Workload 3: Ongoing daily reporting feeds from SaaS advertising platforms (Google Ads and Salesforce) that must be automatically ingested on a recurring schedule directly into analytical tables without maintaining custom ETL infrastructure. Which combination of services should the organization select for these workloads?

AdvancedProfessional Cloud DevOps Engineer

Your team manages an automated batch analytics pipeline deployed on a Google Cloud Dataproc cluster with autoscaling enabled. During analysis of recent billing alerts, you notice that the cluster consistently scales up during Apache Spark job execution but fails to scale down worker nodes after processing completes, remaining at peak worker capacity and incurring excess compute costs. Observability metrics in Cloud Monitoring show that YARN Pending resource drops to zero and Available resource is high, but Spark executor containers are not being decommissioned because interim data was persisted into memory using DataFrame cache operations. Which corrective action should you implement to resolve this autoscaling failure?

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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

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Choosing managed services (e.g., BigQuery, BigLake, AlloyDB, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore)

Running jobs in multiple regions or zones

Transformations

Planning migration and validation to Google Cloud (e.g., BigQuery Data Transfer Service, Database Migration Service, Transfer Appliance, Google Cloud networking, Datastream)

Infrastructure issues