10.4 Batch Processing Solution Design (Azure Batch, HPC Workloads)

Exam AZ-305: Designing Microsoft Azure Infrastructure Solutions Exam

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1 Introduction & Exam Orientation

1.1 AZ-305 Current Exam Facts & Format 1.2 Candidate Profile, Prerequisites & the AZ-104 Requirement 1.3 Skills Outline, Domain Weights & How to Use This Guide

2 Chapter 2: Logging, Monitoring & Diagnostics Design

2.1 Logging Solution Design 2.2 Log Routing Design 2.3 Azure Monitor Metrics & Alerting Design 2.4 Application Insights & Diagnostic Monitoring Design

3 Chapter 3: Authentication & Authorization Design

3.1 Authentication Solution Design 3.2 Identity Management Design 3.3 Authorization for Azure Resources 3.4 Authorization for On-Premises Resources 3.5 Secrets, Certificates & Key Management Design

4 Chapter 4: Governance, Compliance & Identity Governance Design

4.1 Management Group & Subscription Hierarchy Design 4.2 Resource Group Organization & Tagging Strategy Design 4.3 Compliance Management Design 4.4 Identity Governance Design

5 Chapter 5: Relational Data Storage Design

5.1 Relational Data Storage Selection 5.2 Database Service & Compute Tier Design 5.3 Database Scalability Design 5.4 Relational Data Protection Design

6 Chapter 6: Semi-Structured & Unstructured Data Storage Design

6.1 Semi-Structured Data Storage Design 6.2 Unstructured Data Storage Design 6.3 Storage Tier & Cost-Performance Trade-off Design 6.4 Unstructured Data Protection & Durability Design

7 Chapter 7: Data Integration & Analytics Design

7.1 Data Integration Pipeline Design 7.2 Event-Based & Real-Time Data Integration Design 7.3 Data Analytics Platform Design

8 Chapter 8: Backup & Disaster Recovery Design

8.1 Recovery Objectives Design: Calculating and Meeting RTO/RPO 8.2 Azure Site Recovery Architecture for Hybrid Workloads 8.3 Compute Backup & Recovery Design 8.4 Database Backup & Recovery Design 8.5 Unstructured Data Backup & Recovery Design

9 Chapter 9: High Availability Design

9.1 Compute High Availability: Availability Sets & Availability Zones 9.2 Compute High Availability: Load Balancing & VM Scale Set Design 9.3 Relational Data High Availability Design 9.4 Semi-Structured & Unstructured Data High Availability Design

10 Chapter 10: Compute Solution Design

10.1 Compute Requirements Analysis & Virtual Machine-Based Solution Design 10.2 Container-Based Solution Design (AKS, Container Apps, Azure Container Instances) 10.3 Serverless-Based Solution Design (Azure Functions, Logic Apps) 10.4 Batch Processing Solution Design (Azure Batch, HPC Workloads)

11 Chapter 11: Application Architecture Design

11.1 Messaging Architecture Design (Service Bus, Queue Storage) 11.2 Event-Driven Architecture Design (Event Grid, Event Hub) 11.3 API Integration & Application Caching Design (API Management, Azure Cache for Redis, CDN) 11.4 Application Configuration Management & Automated Deployment Design (App Configuration, DevOps Pipelines, Deployment Slots)

12 Chapter 12: Migration Design

12.1 Migration Assessment: Cloud Adoption Framework & On-Premises Evaluation 12.2 IaaS & PaaS Workload Migration Design 12.3 Database Migration Design (Data Migration Service, Database Migration Assistant) 12.4 Unstructured Data Migration Design (AzCopy, Data Box, Storage Migration Service)

13 Chapter 13: Network Solution Design

13.1 Internet Connectivity Design 13.2 Hybrid Connectivity Design 13.3 Network Security Optimization Design 13.4 Network Performance Optimization & Load-Balancing/Routing Design

14 Chapter 14: Final Review, Exam Strategy & Career Path

14.1 Cross-Domain Scenario Practice: Combining Identity, Data, Continuity & Infrastructure Decisions 14.2 Common AZ-305 Decision Traps 14.3 Final Study Plan & Practice Assessment Strategy 14.4 After AZ-305: Retake Policy, Renewal & Career Path

10.4 Batch Processing Solution Design (Azure Batch, HPC Workloads)

Key Takeaways Explain This Topic AI

Azure Batch fits embarrassingly parallel, independent tasks; distinguish this from tightly-coupled HPC workloads needing low-latency inter-node communication.

Azure Batch's low-priority node tier was retired and migrated to Spot nodes by March 2026 — current scenarios should use Spot terminology, not low-priority.

HB-series and HC-series VMs pair with InfiniBand RDMA networking for MPI-style tightly-coupled workloads like CFD and weather simulation.

Azure CycleCloud orchestrates HPC clusters integrating with existing schedulers (Slurm, PBS Pro, Grid Engine, HTCondor) — ideal for lift-and-shift migrations.

Azure Managed Lustre replaced Azure HPC Cache (retired September 30, 2025) as the current recommended parallel file system for I/O-intensive HPC and AI training.

Last updated: July 2026

Why Batch Processing Design Matters on AZ-305

The final compute-selection objective is "Recommend a compute solution for batch processing." It covers fewer raw exam bullets than VM, container, or serverless design, but scenario questions on large-scale parallel job scheduling, Spot-node cost optimization, and migrating legacy on-premises high-performance computing (HPC) clusters appear consistently — particularly for financial services, manufacturing, and life-sciences customers, who are heavy Azure HPC adopters. The exam expects you to distinguish embarrassingly parallel batch workloads (independent tasks with no communication between them) from tightly-coupled HPC workloads (nodes that must communicate with each other at very low latency during the computation).

Azure Batch Core Concepts

Azure Batch is a managed service for running large-scale parallel and high-throughput computing workloads without building your own job-scheduling infrastructure. Its building blocks:

Pools — a collection of compute nodes (VMs) provisioned from an Azure-managed image or a custom image, sized to the workload

Nodes — individual VMs in a pool; can be dedicated (guaranteed capacity, full price) or Spot (discounted, evictable with short notice)

Jobs and Tasks — a job is a logical grouping of related tasks; each task runs on one node. Task dependencies, automatic retries, and a "job manager task" (a controlling task that can submit further tasks) are all configurable

Autoscale formulas — custom scaling expressions that grow or shrink the pool based on the number of pending tasks, rather than a fixed schedule

A critical, exam-current fact: Batch's "low-priority" node tier was retired , with existing low-priority nodes migrated to Spot nodes by March 2026. Any scenario using the term "low-priority nodes" is describing legacy terminology — as of the current blueprint, discounted, evictable Batch capacity is always provisioned as Spot nodes , and Batch-managed allocation-mode pools no longer auto-provision Spot the way they once auto-provisioned low-priority capacity.

HPC-Specific Constructs

Batch is the right tool for embarrassingly parallel work — independent tasks that never need to talk to each other mid-computation. But tightly-coupled HPC workloads (computational fluid dynamics, weather simulation, seismic processing) need nodes that can communicate with extremely low latency during the run, which requires different building blocks entirely:

HB-series / HC-series VMs — the highest per-VM core counts in Azure, paired with InfiniBand remote direct memory access (RDMA) networking for the low-latency, high-bandwidth interconnect that Message Passing Interface (MPI) workloads require between nodes

Azure CycleCloud — an orchestration tool purpose-built for standing up and managing HPC clusters that integrate with existing job schedulers such as Slurm, PBS Pro, Grid Engine, and HTCondor . CycleCloud autoscales a mixed pool of Spot and regular-priority VMs and is the natural fit for lifting and shifting an existing on-premises HPC scheduler into Azure without rewriting job scripts into Batch's job/task model

Azure Managed Lustre — the current recommended parallel file system for I/O-intensive HPC and AI training workloads, delivering throughput in the hundreds of GBps and integrating with Blob Storage for data tiering. It replaces Azure HPC Cache , which was fully retired on September 30, 2025 — recommending HPC Cache in any current-dated scenario is now a wrong answer

Decision Table

Requirement Recommended service

Independent, stateless parallel tasks with a simple job/task model Azure Batch (Spot nodes for cost optimization)

Tightly-coupled MPI workload, or lift-and-shift of an existing on-premises scheduler Azure CycleCloud (with HB/HC-series VMs)

Shared, low-latency parallel file system for I/O-heavy HPC or AI training Azure Managed Lustre

Full custom orchestration outside Batch's task model Virtual Machine Scale Sets with a mixed Spot/regular allocation

Common Exam Traps

Recommending plain Azure Batch for a tightly-coupled MPI simulation that explicitly needs low-latency inter-node communication — the "tightly coupled" language in the scenario stem is the signal to reach for HB/HC-series VMs (via CycleCloud or a Batch pool explicitly configured for InfiniBand), not a default Batch pool.

Using the term "low-priority nodes" as if it is still current — the objective as of the April 2026 blueprint reflects Spot-only terminology; low-priority nodes were retired and migrated.

Recommending Azure HPC Cache for any current scenario — it is retired; Azure Managed Lustre is the current answer for a parallel HPC file system.

Defaulting to Azure Batch when the scenario describes millions of very small, sub-second tasks rather than large or long-running tasks — at that scale, an event-driven Functions/queue design can have lower per-task overhead than Batch's job-scheduling model; the exam tests whether you notice task size and volume, not just "batch" in the prompt.

Scenario Walkthrough

A genomics research firm needs to process 50,000 independent DNA sequencing files nightly, with no dependency between files and full tolerance for a node being evicted mid-task as long as the task simply retries — the fit is an Azure Batch pool using Spot nodes , with an autoscale formula tied to the count of pending tasks. A separate engineering team is migrating an on-premises 200-node Slurm-scheduled computational fluid dynamics cluster and wants to preserve its existing job scripts while running on HB-series VMs with InfiniBand across Azure regions — the fit is Azure CycleCloud , paired with Azure Managed Lustre for the shared low-latency storage the simulation reads and writes during the run.