An enterprise runs a nightly data transformation workload on Google Cloud. The architecture must satisfy the following technical and financial constraints: The nightly batch pipeline is stateless, highly fault-tolerant, and checkpoints state to object storage every 10 minutes. Compute costs for the 6-hour overnight processing window must be minimized as aggressively as possible. The transformed final reports must be actively queried throughout the business day by international business intelligence teams located in North America and Europe. The storage layer hosting the active daily reports requires high availability with a 99.99% availability Service Level Agreement (SLA). Which combination of compute and storage architectures represents the most cost-effective design that satisfies all availability and performance requirements?

Execute the batch pipeline on a Managed Instance Group using Spot VMs, and store the output reporting data in a Multi-Region Standard Cloud Storage bucket: ### What Spot VMs and Multi-Region Standard Storage Are **Spot Virtual Machines (Spot VMs)** are spare Compute Engine capacity offered at significant discounts (60–91% compared to standard on-demand pricing), with the trade-off that Google Cloud can preempt them when capacity is needed elsewhere. **Standard Cloud Storage in a Multi-Region configuration** provides low-latency, highly durable, and geo-redundant object storage across multiple metropolitan areas with a **99.99% availability SLA**. ### How It Meets the Requirements - **Compute Cost Minimization**: Spot VMs provide the highest possible cost savings for compute. Because the workload runs for only 6 hours overnight and checkpoints progress every 10 minutes, preemption causes minimal disruption and zero data loss. - **Global Low-Latency Access**: Multi-region bucket locations distribute data across multiple geographic regions, allowing international analytics teams in North America and Europe to query active datasets efficiently. - **Active Query Performance**: Standard storage class has no data retrieval fees or minimum retention requirements, making it the most cost-effective tier for files that are accessed frequently throughout the business day. - **High Availability (99.99% SLA)**: Multi-Region Cloud Storage provides the required 99.99% availability SLA through cross-region redundancy. ### Key Advantages - **Steep Compute Discounts**: Up to 91% cost reduction on batch compute without long-term resource commitments. - **Fault-Tolerant Resilience**: Automated instance recreation via Managed Instance Groups (MIGs) when paired with frequent application checkpointing. - **Zero Retrieval Fees**: Avoids unexpected operational expenses when analytics teams perform frequent read operations. - **Geo-Redundancy**: Transparent failover and geo-distributed read performance across continents. ### Why This Is Optimal This architecture pairs the cheapest possible transient compute tier with the highest-availability active storage tier, perfectly balancing financial constraints during data generation with rigorous availability and access requirements during data consumption.

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Cost Availability Tradeoffs Spot Vms Multiregion Storage

Section: Design decision trade-offs

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An enterprise runs a nightly data transformation workload on Google Cloud. The architecture must satisfy the following technical and financial constraints:

The nightly batch pipeline is stateless, highly fault-tolerant, and checkpoints state to object storage every 10 minutes.
Compute costs for the 6-hour overnight processing window must be minimized as aggressively as possible.
The transformed final reports must be actively queried throughout the business day by international business intelligence teams located in North America and Europe.
The storage layer hosting the active daily reports requires high availability with a 99.99% availability Service Level Agreement (SLA).

Which combination of compute and storage architectures represents the most cost-effective design that satisfies all availability and performance requirements?

Execute the batch pipeline on a Managed Instance Group using Spot VMs, and store the output reporting data in a Multi-Region Coldline Cloud Storage bucket

Execute the batch pipeline on a Managed Instance Group using Spot VMs, and store the output reporting data in a Multi-Region Standard Cloud Storage bucket

Execute the batch pipeline on standard Compute Engine instances covered by 3-year Committed Use Discounts (CUDs), and store the output reporting data in a Single-Region Standard Cloud Storage bucket

Execute the batch pipeline on preemptible Compute Engine VMs, and store the output reporting data in a Dual-Region Archive Cloud Storage bucket

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Cost Availability Tradeoffs Spot Vms Multiregion Storage

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