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AZ-305 Compute solutions

April 30, 2025•1318 words•7 min read•
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AZ-305 Azure Solutions Architect Expert

Azure Recommendations for Compute Solutions

Below are the recommended Azure solutions for compute as aligned with the AZ-305 exam objectives.

Design Compute Solutions in Azure

1. Specify Components of a Compute Solution Based on Workload Requirements

Evaluate workload needs across these dimensions:

  • Scalability: Horizontal (scale-out) vs. vertical (scale-up).
  • Performance: CPU/memory/GPU requirements, latency sensitivity.
  • Cost: Pay-as-you-go vs. reserved instances, spot pricing for batch jobs.
  • Management Overhead: Fully managed (PaaS) vs. self-managed (IaaS).
  • Compliance: Dedicated hardware (Azure Dedicated Hosts) or isolated containers.

Key Components:

  • Compute Service (VMs, containers, serverless).
  • Orchestration (VM Scale Sets, Kubernetes).
  • Networking (load balancers, VNet integration).
  • Storage (managed disks, Blob Storage).
  • Monitoring/Logging (Azure Monitor, Log Analytics).

2. Recommend a Virtual Machine-Based Solution

Azure Virtual Machine Scale Sets (VMSS) is optimal for most VM workloads:

  • Auto-scaling: Adjust VM count based on CPU/memory metrics12.
  • High Availability: Distribute VMs across Availability Zones (99.99% SLA)23.
  • Heterogeneous Workloads: Mix VM sizes in a single scale set2.
  • Use Cases:
    • Stateful apps (e.g., databases) with uniform orchestration.
    • Lift-and-shift legacy apps requiring full OS control.

Alternatives:

  • Azure Dedicated Hosts: For regulatory compliance or legacy licensing34.
  • Single VMs: Low-traffic apps with static resource needs.

3. Recommend a Container-Based Solution

ScenarioRecommendationKey Features
Advanced OrchestrationAzure Kubernetes Service (AKS)Full Kubernetes API access, auto-scaling pods/nodes, integrates with Azure AD and monitoring56.
Serverless ContainersAzure Container AppsNo infrastructure management, auto-scaling to zero, Dapr integration for microservices56.
Burst/Short-Lived TasksAzure Container Instances (ACI)Launch containers in seconds, hypervisor isolation, cost-effective for sporadic workloads74.

Best Practices:

  • Use AKS for complex microservices requiring custom networking/storage.
  • Prefer Container Apps for event-driven APIs or background jobs6.

4. Recommend a Serverless-Based Solution

Azure Functions:

  • Event-Driven Workloads: HTTP triggers, timer-based jobs, or IoT telemetry processing83.
  • Cost Optimization: Pay per execution (millisecond billing), scale to zero during inactivity86.
  • Integration: Connect to Azure Event Grid, Cosmos DB, or Blob Storage8.

Azure Logic Apps:

  • Workflow Automation: Low-code/no-code integration with SaaS apps (e.g., Salesforce, SharePoint)8.

Limitations:

  • Avoid for long-running processes (>10 minutes) or high-memory apps6.

5. Recommend a Compute Solution for Batch Processing

Azure Batch:

  • Parallel Jobs: Process large datasets (e.g., financial simulations, media transcoding) with auto-scaling pools94.
  • Hybrid Bursting: Combine with ACI for on-demand compute during peak loads79.
  • Cost Savings: Use low-priority VMs for fault-tolerant workloads9.

Use Cases:

  • Genomics analysis, Monte Carlo risk modeling, 3D rendering9.

Decision Table: Compute Solutions

Workload TypeRecommendationScalabilityManagementCost Efficiency
Legacy AppsVM Scale SetsHigh (1,000 nodes)ModerateMedium (reserved instances)
MicroservicesAKS/Container AppsHigh (K8s pods)Low (PaaS)High (serverless containers)
Event-DrivenAzure FunctionsAutomaticFully managedPay-per-use
Batch/HPCAzure Batch + ACIMassive parallelModerateHigh (spot instances)

Key Considerations

  • Stateless Workloads: Prefer serverless or containers for faster scaling58.
  • Compliance: Use Dedicated Hosts or isolated containers for regulatory needs34.
  • Cost Control: Apply Azure Policy to enforce VM size limits and tag-based governance10.

By aligning these recommendations with workload requirements, you optimize performance, cost, and operational efficiency in Azure.

Summarised with Perplexity.

Footnotes

  1. https://learn.microsoft.com/en-us/azure/architecture/guide/technology-choices/compute-decision-tree ↩
  2. https://azure.microsoft.com/en-gb/products/virtual-machine-scale-sets ↩ ↩2 ↩3
  3. https://k21academy.com/microsoft-azure/az-104/understanding-azure-compute-services/ ↩ ↩2 ↩3 ↩4
  4. https://www.alifconsulting.com/post/azure-compute-service ↩ ↩2 ↩3 ↩4
  5. https://learn.microsoft.com/en-us/azure/architecture/guide/choose-azure-container-service ↩ ↩2 ↩3
  6. https://cast.ai/blog/azure-containers-services-pricing-and-feature-comparison/ ↩ ↩2 ↩3 ↩4 ↩5
  7. https://azure.microsoft.com/en-gb/products/container-instances ↩ ↩2
  8. https://azure.microsoft.com/en-us/solutions/serverless ↩ ↩2 ↩3 ↩4 ↩5
  9. https://learn.microsoft.com/en-us/azure/batch/batch-technical-overview ↩ ↩2 ↩3 ↩4
  10. https://learn.microsoft.com/en-us/azure/well-architected/performance-efficiency/select-services ↩

If you want to get in touch and hear more about this topic, feel free to contact me on or via .

© 2026 Andrei Bodea

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