An enterprise is planning to migrate three distinct data workloads to Google Cloud to support an analytics initiative: Workload 1: 250 TB of historical video archives stored on an on-premises NFS file server. The facility has an outbound internet uplink of only 20 Mbps, and acquiring additional bandwidth is cost-prohibitive. Workload 2: 45 TB of raw image assets residing in an Amazon S3 bucket that must be transferred online to a Google Cloud Storage bucket on a recurring schedule. Workload 3: Ongoing customer relationship data stored in Salesforce that needs to be automatically loaded directly into BigQuery on a daily schedule without custom pipeline code. Which combination of Google Cloud services should you recommend for these workloads?

Workload 1: Transfer Appliance; Workload 2: Storage Transfer Service; Workload 3: BigQuery Data Transfer Service: What the Recommended Services Are This architecture pairs Google Cloud's purpose-built transfer solutions to their ideal data sources, locations, network constraints, and destinations: Transfer Appliance: A high-capacity physical storage device shipped to a customer's data center to perform offline data uploads directly to Cloud Storage. Storage Transfer Service: A fully managed cloud service that automates large-scale online object and file movement from cloud providers (like Amazon S3 and Azure Blob) and on-premises storage to Cloud Storage. BigQuery Data Transfer Service (DTS): A managed service that automates scheduled data movement from Software-as-a-Service (SaaS) applications, supported databases, and external cloud storage directly into BigQuery. How This Solution Meets Each Requirement Workload 1 (Transfer Appliance): Transferring 250 TB over an unupgraded 20 Mbps connection would take several years to complete online. Transfer Appliance enables an offline migration that bypasses the bandwidth bottleneck securely and cost-effectively. Workload 2 (Storage Transfer Service): Storage Transfer Service provides scalable, managed cloud-to-cloud data ingestion from Amazon S3 directly to Cloud Storage without requiring intermediary compute infrastructure. Workload 3 (BigQuery Data Transfer Service): DTS has native, code-free SaaS connectors (including Salesforce) to ingest and schedule daily updates straight into BigQuery destination tables. Key Advantages Optimized Network Utilization: Prevents network saturation on slow on-premises links while leveraging cloud provider backbones for cloud-to-cloud transfers. Operational Efficiency: Eliminates the need to maintain custom scripts, virtual machines, or ETL pipelines for third-party cloud and SaaS synchronization. Built-in Resilience: Storage Transfer Service and BigQuery Data Transfer Service feature automatic retries, scheduling, and error reporting. Why This Is Optimal Choosing the right migration tool based on data volume, bandwidth availability, source system, and target platform delivers the lowest operational cost and quickest time to deployment.

Professional Cloud Architect

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Intermediate

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Intermediate

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Transfer Appliance, Storage Transfer Service and BigQuery DTS

Section: Data and system migration and management tooling

...

Explanations

...

View source documentation

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Last updated August 28, 2026

Professional Cloud Architect

Intrigued by the art of cloud architecture? Discover how to design, develop, and manage robust, secure, scalable, and dynamic solutions on Google Cloud as you prepare for the Professional Cloud Architect exam!

Intermediate

Prepare and test your skills

Intermediate

Prepare and test your skills

Transfer Appliance, Storage Transfer Service and BigQuery DTS

Section: Data and system migration and management tooling

An enterprise is planning to migrate three distinct data workloads to Google Cloud to support an analytics initiative:

Workload 1: 250 TB of historical video archives stored on an on-premises NFS file server. The facility has an outbound internet uplink of only 20 Mbps, and acquiring additional bandwidth is cost-prohibitive.
Workload 2: 45 TB of raw image assets residing in an Amazon S3 bucket that must be transferred online to a Google Cloud Storage bucket on a recurring schedule.
Workload 3: Ongoing customer relationship data stored in Salesforce that needs to be automatically loaded directly into BigQuery on a daily schedule without custom pipeline code.

Which combination of Google Cloud services should you recommend for these workloads?

...

Workload 1: Storage Transfer Service; Workload 2: Transfer Appliance; Workload 3: Dataproc

...

Workload 1: Transfer Appliance; Workload 2: Storage Transfer Service; Workload 3: BigQuery Data Transfer Service

...

Workload 1: Transfer Appliance; Workload 2: BigQuery Data Transfer Service; Workload 3: Storage Transfer Service

...

Workload 1: Google Cloud CLI (gcloud storage); Workload 2: Storage Transfer Service; Workload 3: Cloud Data Fusion

...

Explanations

What the Recommended Services Are

This architecture pairs Google Cloud's purpose-built transfer solutions to their ideal data sources, locations, network constraints, and destinations:

Transfer Appliance: A high-capacity physical storage device shipped to a customer's data center to perform offline data uploads directly to Cloud Storage.
Storage Transfer Service: A fully managed cloud service that automates large-scale online object and file movement from cloud providers (like Amazon S3 and Azure Blob) and on-premises storage to Cloud Storage.
BigQuery Data Transfer Service (DTS): A managed service that automates scheduled data movement from Software-as-a-Service (SaaS) applications, supported databases, and external cloud storage directly into BigQuery.

How This Solution Meets Each Requirement

Workload 1 (Transfer Appliance): Transferring 250 TB over an unupgraded 20 Mbps connection would take several years to complete online. Transfer Appliance enables an offline migration that bypasses the bandwidth bottleneck securely and cost-effectively.
Workload 2 (Storage Transfer Service): Storage Transfer Service provides scalable, managed cloud-to-cloud data ingestion from Amazon S3 directly to Cloud Storage without requiring intermediary compute infrastructure.
Workload 3 (BigQuery Data Transfer Service): DTS has native, code-free SaaS connectors (including Salesforce) to ingest and schedule daily updates straight into BigQuery destination tables.

Key Advantages

Optimized Network Utilization: Prevents network saturation on slow on-premises links while leveraging cloud provider backbones for cloud-to-cloud transfers.
Operational Efficiency: Eliminates the need to maintain custom scripts, virtual machines, or ETL pipelines for third-party cloud and SaaS synchronization.
Built-in Resilience: Storage Transfer Service and BigQuery Data Transfer Service feature automatic retries, scheduling, and error reporting.

Why This Is Optimal

Choosing the right migration tool based on data volume, bandwidth availability, source system, and target platform delivers the lowest operational cost and quickest time to deployment.

...

View source documentation

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IntermediateProfessional Cloud Architect

An enterprise organization wants to streamline infrastructure provisioning on Google Cloud while eliminating manual interventions. Currently, engineers execute terraform apply directly from their local workstations, leading to configuration drift and inconsistent environments between staging and production. The cloud architecture team establishes the following governance and operational requirements: All infrastructure changes must be managed as code in a centralized Git repository. Proposed configuration changes must be automatically previewed and validated during code review before merging. Deployments must be automated via continuous integration and delivery (CI/CD) pipelines, ensuring changes are deployed and verified in the dev environment prior to promotion to prod. Remote state files must be centrally maintained and protected. How should you design this automated provisioning workflow?

Service catalog and provisioning

IntermediateProfessional Cloud Architect

CMEK Service Agent CryptoKey Encrypter/Decrypter Role

Security and access management

AdvancedProfessional Cloud DevOps Engineer

Your team manages a critical microservice monitored via Cloud Service Mesh and Cloud Monitoring. The service has the following configuration and operational metrics recorded over a rolling 30-day compliance window: Total Requests: 20,000,000 Availability SLO Target: 99.9% successful requests Recorded Unsuccessful Requests (Bad Events): 8,000 What is the remaining error budget expressed as a percentage of the total allocated error budget, and what site reliability engineering (SRE) action should the team take?

Error budgets (e.g., Cloud Service Mesh definitions)

Want more questions like this?

Get a free certification question every week.

Last updated August 28, 2026

An enterprise is planning to migrate three distinct data workloads to Google Cloud to support an analytics initiative:

Workload 1: 250 TB of historical video archives stored on an on-premises NFS file server. The facility has an outbound internet uplink of only 20 Mbps, and acquiring additional bandwidth is cost-prohibitive.
Workload 2: 45 TB of raw image assets residing in an Amazon S3 bucket that must be transferred online to a Google Cloud Storage bucket on a recurring schedule.
Workload 3: Ongoing customer relationship data stored in Salesforce that needs to be automatically loaded directly into BigQuery on a daily schedule without custom pipeline code.

Which combination of Google Cloud services should you recommend for these workloads?

Workload 1: Storage Transfer Service; Workload 2: Transfer Appliance; Workload 3: Dataproc

Workload 1: Transfer Appliance; Workload 2: Storage Transfer Service; Workload 3: BigQuery Data Transfer Service

Workload 1: Transfer Appliance; Workload 2: BigQuery Data Transfer Service; Workload 3: Storage Transfer Service

Workload 1: Google Cloud CLI (gcloud storage); Workload 2: Storage Transfer Service; Workload 3: Cloud Data Fusion

Workload 1: Storage Transfer Service; Workload 2: Transfer Appliance; Workload 3: Dataproc

Workload 1: Transfer Appliance; Workload 2: Storage Transfer Service; Workload 3: BigQuery Data Transfer Service

What the Recommended Services Are

This architecture pairs Google Cloud's purpose-built transfer solutions to their ideal data sources, locations, network constraints, and destinations:

Transfer Appliance: A high-capacity physical storage device shipped to a customer's data center to perform offline data uploads directly to Cloud Storage.
Storage Transfer Service: A fully managed cloud service that automates large-scale online object and file movement from cloud providers (like Amazon S3 and Azure Blob) and on-premises storage to Cloud Storage.
BigQuery Data Transfer Service (DTS): A managed service that automates scheduled data movement from Software-as-a-Service (SaaS) applications, supported databases, and external cloud storage directly into BigQuery.

How This Solution Meets Each Requirement

Workload 1 (Transfer Appliance): Transferring 250 TB over an unupgraded 20 Mbps connection would take several years to complete online. Transfer Appliance enables an offline migration that bypasses the bandwidth bottleneck securely and cost-effectively.
Workload 2 (Storage Transfer Service): Storage Transfer Service provides scalable, managed cloud-to-cloud data ingestion from Amazon S3 directly to Cloud Storage without requiring intermediary compute infrastructure.
Workload 3 (BigQuery Data Transfer Service): DTS has native, code-free SaaS connectors (including Salesforce) to ingest and schedule daily updates straight into BigQuery destination tables.

Key Advantages

Optimized Network Utilization: Prevents network saturation on slow on-premises links while leveraging cloud provider backbones for cloud-to-cloud transfers.
Operational Efficiency: Eliminates the need to maintain custom scripts, virtual machines, or ETL pipelines for third-party cloud and SaaS synchronization.
Built-in Resilience: Storage Transfer Service and BigQuery Data Transfer Service feature automatic retries, scheduling, and error reporting.

Why This Is Optimal

Choosing the right migration tool based on data volume, bandwidth availability, source system, and target platform delivers the lowest operational cost and quickest time to deployment.

Workload 1: Transfer Appliance; Workload 2: BigQuery Data Transfer Service; Workload 3: Storage Transfer Service

Workload 1: Google Cloud CLI (gcloud storage); Workload 2: Storage Transfer Service; Workload 3: Cloud Data Fusion

Professional Cloud Architect

Transfer Appliance, Storage Transfer Service and BigQuery DTS

Related questions

Declarative Infrastructure as Code

Migrate to Virtual Machines Test Clones

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Professional Cloud Architect

Transfer Appliance, Storage Transfer Service and BigQuery DTS

What the Recommended Services Are

How This Solution Meets Each Requirement

Key Advantages

Why This Is Optimal

Related questions

Declarative Infrastructure as Code

Migrate to Virtual Machines Test Clones

Recommender API Billing Project

CMEK Service Agent CryptoKey Encrypter/Decrypter Role

What the Recommended Services Are

How This Solution Meets Each Requirement

Key Advantages

Why This Is Optimal