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Professional Cloud Data Engineer
Capacity management (e.g., BigQuery Editions and reservations)
A financial services institution is architecting a centraliz…

Professional Cloud Data Engineer

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Sample Question
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Description
Sample Question
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Practice Test

Evaluate Select Bigquery Editions Workloads

Section: Capacity management (e.g., BigQuery Editions and reservations)

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Related questions

Keep the momentum going with these hand-picked practice scenarios

AdvancedProfessional Cloud Data Engineer

An enterprise data platform team manages workloads on BigQuery Enterprise edition in the us-central1 region across two separate reservations: batch_etl: Configured with a baseline of 600 slots, a maximum reservation size of 1,200 slots (allowing up to 600 autoscale slots), and idle slot sharing enabled (ignore_idle_slots = false). reporting_bi: Configured with a baseline of 400 slots, a maximum reservation size of 800 slots (allowing up to 400 autoscale slots), and idle slot sharing enabled (ignore_idle_slots = false). At 03:00 UTC, the reporting_bi reservation is completely idle with 0 active jobs. An unpredicted ETL job begins running in the batch_etl reservation, requiring 1,100 compute slots to process concurrently. In what order will BigQuery allocate compute capacity to satisfy the 1,100 slot demand, and how will this compute capacity be billed?

Capacity management (e.g., BigQuery Editions and reservations)

AdvancedProfessional Cloud Data Engineer

An enterprise is operating BigQuery on the Enterprise edition across several departments. The data engineering team manages a multi-tenant environment with the following workload requirements: ETL/ELT pipeline jobs: Require strictly guaranteed execution times and predictable slot allocation without interference or unexpected queue delays. Ad-hoc data analyst queries: Experience unpredictable traffic bursts during business hours, but can tolerate idle resource sharing to minimize costs. Which configuration strategy should the team implement across their reservations to satisfy these workload requirements while optimizing cost?

Capacity management (e.g., BigQuery Editions and reservations)

AdvancedProfessional Cloud Data Engineer

An enterprise financial organization is designing an ingestion pipeline to integrate continuous, high-volume transactional logs from an on-premises database into a secured BigQuery data warehouse. The technical, analytical, and compliance requirements are as follows: Throughput & Network: High-bandwidth, low-latency private connectivity to transfer high-velocity streaming records into Google Cloud. Client-Side Data Protection: Highly sensitive columns (such as credit card numbers) must be encrypted on-premises prior to ingestion into the cloud. Granular Decryption & Masking: In BigQuery, authorized fraud investigators must be able to run SQL queries to decrypt the protected data on-demand, whereas general analysts querying the same table must only see dynamically masked values. Key Governance: Encryption keys must be hardware-backed with Cloud HSM and centrally managed via customer-managed encryption keys (CMEK). Which ingestion and security design should the data engineering team implement?

Integrating with new data sources

AdvancedProfessional Cloud Data Engineer

An enterprise runs mission-critical analytical workflows on Cloud Composer in the us-central1 region. The data engineering team must implement an automated cross-region disaster recovery (DR) strategy to fail over orchestration to us-east4 in the event of an outage. The solution must satisfy the following technical requirements: Maintain configuration and version parity across the primary and failover environments. Continuously validate and monitor automated snapshot creation, alerting on any failures. Orchestrate automated snapshot loading, environment configuration updates, and execution handoff during a failover event. Prevent inconsistent state and side effects caused by re-executing tasks that ran after the latest snapshot. Which architecture and recovery workflow should the team deploy?

Disaster recovery and fault tolerance

AdvancedProfessional Cloud Data Engineer

An analytics team at an enterprise company runs complex multi-stage aggregation queries on BigQuery during peak business hours. Recently, several critical ad-hoc and dashboard queries have started failing with the following error: Resources exceeded: Your project or organization exceeded the maximum disk and memory limit available for shuffle operations. You need to diagnose and resolve these query execution failures while maintaining performance and optimizing compute capacity. Which approach should you implement to resolve the shuffle limit error?

Manage workloads, such as jobs, queries, and compute capacity (reservations)

AdvancedProfessional Cloud DevOps Engineer

An enterprise organization operates a multi-tenant Google Cloud environment where multiple development teams share a central CI/CD project. Each team builds containerized microservices and deploys them to dedicated Artifact Registry repositories in their respective workload projects. To meet stringent security compliance standards, the DevOps team must ensure that: CI/CD pipeline triggers strictly enforce least privilege and prevent privilege escalation or cross-tenant access. Build execution permissions are isolated such that a compromised build script cannot access another team's artifacts or cloud resources. Administrative overhead for managing trigger execution identity remains minimal and compliant with Google Cloud security best practices. How should the DevOps engineer configure the Cloud Build triggers and IAM permissions?

CI/CD pipeline triggers

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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 financial services institution is architecting a centralized enterprise data platform on Google Cloud to support mission-critical risk analytics and regulatory reporting. The data platform must satisfy the following technical and governance requirements:

Provide a 99.99% availability Service Level Agreement (SLA) for core query processing.
Enforce advanced security and compliance controls, including Cloud External Key Manager (Cloud EKM) integration and multi-region disaster recovery with automated data replication failover.
Support autoscaling compute capacity with baseline workload predictability by leveraging 3-year Committed Use Discounts (CUDs).
Provide cross-project reservation sharing and centralized workload management across multiple business units.

Which BigQuery Edition and capacity configuration should you select to meet these requirements?

BigQuery Standard Edition configured with autoscaling reservations and 3-year capacity commitments

BigQuery Enterprise Plus Edition configured with autoscaling reservations and 3-year capacity commitments

BigQuery on-demand analysis pricing model configured with physical storage billing and CMEK

BigQuery Enterprise Edition configured with baseline commitments and multi-region failover replication

BigQuery Standard Edition configured with autoscaling reservations and 3-year capacity commitments

BigQuery Enterprise Plus Edition configured with autoscaling reservations and 3-year capacity commitments

What BigQuery Enterprise Plus Edition Is

BigQuery Enterprise Plus Edition is Google Cloud's highest-tier analytical compute offering, engineered for mission-critical workloads that demand the highest levels of performance, availability, enterprise governance, and regulatory compliance.

How It Meets the Requirements

99.99% Availability SLA: Enterprise Plus delivers a high-availability 99.99% uptime SLA, compared to the standard 99.9% SLA available on lower editions.
Advanced Compliance & Security: It supports advanced governance capabilities, including Cloud External Key Manager (Cloud EKM), multi-region disaster recovery failover, FedRAMP High compliance, and fine-grained data protection.
Cost Predictability via CUDs: Enterprise Plus supports 1-year and 3-year Committed Use Discounts (CUDs) for baseline capacity, allowing organizations to reserve slot commitments at discounted rates while using autoscaling for burst workloads.
Centralized Capacity Management: It enables organization-level reservations, slot sharing across multiple projects, and reservation assignments to isolate or share compute resources across business units.

Key Advantages and Capabilities

Disaster Recovery: Native cross-region continuous data replication and managed failover capabilities.
Enhanced Performance: Accelerated compute performance tier with expanded maximum memory and processing capacity per slot.
Flexible Scaling: Dynamic autoscaling slots layered on top of baseline committed slots to optimize price-performance.
Enterprise Governance: Integrated support for Column-level access control, Data Masking, VPC Service Controls, and Cloud EKM.

Why This Is Optimal

Only the Enterprise Plus Edition combines a 99.99% uptime SLA, multi-region disaster recovery, and Cloud EKM security with the financial optimization of multi-year capacity commitments.

BigQuery on-demand analysis pricing model configured with physical storage billing and CMEK

BigQuery Enterprise Edition configured with baseline commitments and multi-region failover replication