A financial analytics company runs large-scale, fault-tolerant nightly batch processing workloads on Google Compute Engine. The processing application can gracefully checkpoint progress, handle node terminations, and run on various CPU architectures and sizes. Management requires a compute architecture that minimizes infrastructure costs using Spot VMs while mitigating the risk of batch processing delays caused by high localized preemption rates or capacity shortages for a single machine type. Which configuration should the cloud architect recommend?

Configure a regional Managed Instance Group (MIG) using Spot VMs configured with an instance flexibility policy specifying multiple compatible machine types.: What Instance Flexibility with Spot VMs is Instance flexibility in a regional Managed Instance Group (MIG) allows the group to provision virtual machines from a list of multiple configured machine types rather than being constrained to a single VM size or family. When applied to Spot VMs, the MIG intelligently selects from the specified machine types and zones based on real-time resource availability and historical stability. How it meets the requirements Preemption mitigation: When a MIG configured with instance flexibility uses Spot VMs, it automatically identifies and selects machine types with the lowest observed preemption rate within the region, ensuring maximum workload longevity and minimizing interruptions. Capacity resiliency: If a specific machine type experiences high demand or temporary stockouts, the MIG dynamically provisions alternative compatible machine types from the flexibility policy list. Cost optimization: The workload takes full advantage of Spot VM pricing (up to 60-91% discount compared to on-demand pricing) while preventing batch completion delays. Key advantages Automated selection: Evaluates current capacity across zones and machine types automatically without manual administrative overhead. Fault tolerance alignment: Complements checkpoint-capable, stateless, and batch processing systems seamlessly. Target distribution: Allows balanced distribution across regional zones to reduce localized failure domains. Ranked preferences: Supports ranking instance selections so the MIG prioritizes the most cost-effective machine types before falling back to larger or alternative tiers. Why this is optimal Using Spot VMs with instance flexibility provides the best balance of aggressive cost reduction and workload reliability for batch processing. Because the application can run across different CPU types and handles terminations gracefully, opening the resource pool across multiple machine types prevents job stalls caused by localized Spot preemption spikes.

Spot Vms Mig Instance Flexibility Batch | Professional Cloud Architect

IntermediateProfessional Cloud Architect

An enterprise artificial intelligence team is deploying distributed large language model (LLM) fine-tuning jobs on Google Cloud. The architecture must satisfy the following technical and operational constraints: The workloads require densely allocated hardware accelerators to minimize inter-node network latency during distributed training. Jobs run periodically for 3 to 5 days and can wait in a queue until resources become available, but once started, they must run to completion without mid-run preemption. The organization wants to optimize compute costs while avoiding multi-month or annual capacity commitments. Which compute provisioning strategy should the cloud architect recommend?

Choosing compute resources (e.g., spot VMs, custom machine types, and specialized workload)

IntermediateProfessional Cloud Architect

A global gaming company is architecting a low-latency caching and state store on Google Cloud to handle millions of active sessions. The engineering team requires horizontal write scalability across multiple shards and needs to scale out read throughput across multiple zones while maintaining minimal client response times. The team decides to deploy Memorystore for Redis Cluster (or Memorystore for Valkey) in Cluster Mode Enabled with read replicas provisioned for each shard, configuring client connections to issue the READONLY command to read directly from replica nodes. According to distributed systems design trade-offs and the CAP theorem, what architectural trade-off must the team accept with this configuration?

Design decision trade-offs

AdvancedProfessional Cloud Security Engineer

Two independent healthcare institutions want to collaborate by training a shared predictive machine learning model using their respective proprietary clinical datasets. The collaboration has the following strict security and governance requirements: Neither organization is permitted to expose raw data or cryptographic decryption keys to the other party. The infrastructure administrator executing the workload must not be able to view data in use in memory, access the underlying operating system, or extract intermediate computation states. Decryption keys stored in Cloud Key Management Service (Cloud KMS) must be released dynamically only after cryptographically verifying the identity, boot measurements, and container image digest of the executing workload. Which Google Cloud Confidential Computing solution should be implemented to support this multi-party collaboration?

Enabling Confidential Computing.

Professional Cloud Architect

Spot Vms Mig Instance Flexibility Batch

Global External Application Load Balancer with Multi-Region Spanner

Global External Load Balancer with Regional MIGs Auto-Healing

Compute Engine Instance Availability Policy

Professional Cloud Architect

Spot Vms Mig Instance Flexibility Batch

What Instance Flexibility with Spot VMs is

How it meets the requirements

Key advantages

Why this is optimal

Global External Application Load Balancer with Multi-Region Spanner

Global External Load Balancer with Regional MIGs Auto-Healing

Compute Engine Instance Availability Policy

What Instance Flexibility with Spot VMs is

How it meets the requirements

Key advantages

Why this is optimal

Professional Cloud Architect

Spot Vms Mig Instance Flexibility Batch

Related questions

Global External Application Load Balancer with Multi-Region Spanner

Global External Load Balancer with Regional MIGs Auto-Healing

Compute Engine Instance Availability Policy

Professional Cloud Architect

Spot Vms Mig Instance Flexibility Batch

What Instance Flexibility with Spot VMs is

How it meets the requirements

Key advantages

Why this is optimal

Related questions

Global External Application Load Balancer with Multi-Region Spanner

Global External Load Balancer with Regional MIGs Auto-Healing

Compute Engine Instance Availability Policy

What Instance Flexibility with Spot VMs is

How it meets the requirements

Key advantages

Why this is optimal