VMware 2V0-31.23 Aria Automation 8.10 Professional: Legacy VCP-CMA Skills and Modern VCF Automation

VMware 2V0-31.23 is the VMware Aria Automation 8.10 Professional exam that led to the VCP-CMA 2024 certification. Broadcom still hosts the official exam guide, which describes 64 items, 130 minutes, and a scaled passing score of 300. Its candidate profile covers installation, configuration, management, troubleshooting, SaaS connectivity, cloud concepts, extensibility, identity, Kubernetes-related integration, and the major Aria Automation services.

The certification landscape has since shifted. Broadcom’s current public VMware certification catalog emphasizes VCF/VVF professional tracks and advanced VCF automation rather than the older VCP-CMA career track. Candidates should therefore treat 2V0-31.23 as a legacy-era automation exam while preserving the automation principles that remain relevant to modern private-cloud operations.

Current VMware certifications are the right reference for deciding what to pursue now. The 2V0-31.23 material remains useful for understanding what the exam taught and how those skills map forward.

Aria Automation turns infrastructure standards into reusable services

The core idea is self-service infrastructure with governance. Instead of treating every request as a manual ticket, administrators define reusable templates, policies, projects, cloud zones, images, flavors, networks, and catalog items. Consumers request approved services while the platform applies the organization’s placement, access, and lifecycle rules.

The value depends on the standards being encoded. A poorly designed template simply automates inconsistency. Strong candidates understand how automation makes an operating model repeatable and how governance prevents self-service from becoming uncontrolled provisioning.

Cloud Assembly requires understanding placement as well as syntax

Templates describe desired resources, but the platform still has to decide where those resources can be deployed. Projects, cloud zones, constraints, tags, mappings, profiles, and quotas influence placement. A deployment failure may therefore be caused by policy or capacity even when the template itself is valid.

Practice reading a requirement and predicting which construct should express it. Use tags for matching capabilities, policies for governance, mappings to abstract environment-specific details, and project boundaries for access and resource scope. This is more durable than memorizing example YAML line by line.

Service Broker and catalogs separate consumption from implementation

Consumers should not need to know every infrastructure detail. Catalogs expose approved services with forms, policies, entitlements, and lifecycle actions. Administrators decide what users can request and what happens after deployment. This creates a controlled interface between platform engineering and application teams.

Think about catalog design as a product problem. Inputs should be understandable, defaults should be safe, policies should prevent unsupported combinations, and lifecycle actions should cover common operational needs. A technically valid service that users cannot understand will still generate manual work.

Orchestrator and extensibility handle workflows beyond basic provisioning

VMware Aria Automation Orchestrator and extensibility mechanisms allow automation to call external systems, run custom workflows, react to events, and integrate with processes that are not native deployment actions. The exam expects candidates to understand where these mechanisms fit and which problems they are appropriate for.

Apply general workflow-automation principles: make actions idempotent where practical, validate inputs, handle failures, log outcomes, protect secrets, and define rollback or compensating steps. Extensibility is most valuable when it is supportable after the original author leaves.

Pipelines connect infrastructure automation with delivery workflows

Aria Automation Pipelines introduced a delivery-oriented model with stages and tasks that can orchestrate build, test, approval, and deployment activities. Candidates should understand execution order, dependencies, variables, endpoints, and how a pipeline interacts with infrastructure or application tooling.

This overlaps with broader DevOps pipeline practices. The important preparation skill is recognizing where governance and validation belong so automation accelerates safe delivery rather than merely reducing the number of manual clicks.

Identity, projects, and policy determine who can automate what

Self-service increases the importance of access boundaries. Administrators should understand how users and groups receive project access, how entitlements restrict catalog consumption, and how policies can control approvals, leases, resource actions, and placement. Least privilege matters because automation can create or modify resources at scale.

Review cloud identity and access fundamentals and apply them to human users, service accounts, API tokens, and integrations. A credential used by automation is an operational dependency and must be rotated and monitored like any other privileged identity.

Troubleshooting automation requires following the request through its stages

When a deployment fails, identify whether the issue is request validation, policy, placement, template syntax, cloud-zone availability, network or storage mapping, authentication, provider connectivity, extensibility, or the target infrastructure. The UI error is only a starting point; logs and execution history provide the sequence of events.

Build failure scenarios deliberately. Remove a required tag, break a credential, exhaust a quota, misconfigure a network profile, or make an extensibility action fail. Predict the resulting symptom, then verify it. This is far more effective than studying only successful deployments.

Kubernetes and multi-cloud concepts matter because automation targets diverse platforms

The official 2V0-31.23 candidate profile includes basic Kubernetes knowledge and public/private/hybrid cloud concepts. Administrators need to recognize that different targets expose different capabilities, identity models, networking, storage, and lifecycle behavior. Abstraction can simplify consumption, but it cannot erase every platform difference.

A review of Kubernetes fundamentals helps explain the cluster and namespace concepts that appear when automation extends beyond VMs. Focus on the infrastructure interfaces rather than application-development detail.

Modern VCF automation keeps the principles but changes the credential path

Broadcom’s current certification model includes VCF Automation at the advanced professional level and includes automation services within the broader VCF platform. That direction makes the old VCP-CMA exam useful as a foundation in templates, policy, orchestration, pipelines, identity, and troubleshooting, but not a reliable guide to today’s certification registration.

Use the VCF certification roadmap to choose the current destination. Preserve the engineering habits from 2V0-31.23—repeatability, governance, safe extensibility, observability, and lifecycle thinking—while learning the current VCF Automation services and exam objectives.

Configuration management is another useful part of the automation mindset. Desired-state systems help keep operating systems and applications consistent after deployment, while provisioning tools create or modify infrastructure. Candidates should understand the boundary because a template that creates a VM does not automatically guarantee the software inside remains compliant for its entire life.

Secrets should never be treated as ordinary template variables. Passwords, tokens, API keys, and certificates need controlled storage, scoped access, rotation, and auditing. When an automation workflow calls several systems, the number of credentials can grow quickly, so credential design becomes part of reliability as well as security.

Testing should cover both the happy path and expected failure modes. Validate templates against multiple environments, check policy interactions, simulate quota exhaustion, test expired or revoked credentials, and confirm what happens if an external endpoint is unavailable. A service is not production ready simply because its first deployment succeeds.

Drift and lifecycle deserve explicit attention. Resources can be changed outside the automation platform, providers can introduce new behavior, images age, and integrations change API versions. Define how the team detects drift, decides which system is authoritative, updates templates, and retires obsolete services without stranding existing deployments.

Finally, measure automation by operational outcomes. Useful indicators include deployment success rate, time to provision, failed-policy requests, manual interventions, rollback frequency, stale resources, and recovery time after workflow failures. These metrics reveal whether automation is actually improving service delivery or simply moving complexity into a different interface.

Network and IP address management integrations deserve focused study because they sit directly on the deployment path. A request can succeed through template validation and still fail when an address pool is exhausted, a DNS update is rejected, or a network profile maps to the wrong environment. Trace these dependencies explicitly and include their health in platform monitoring.

Image management is another lifecycle concern. Golden images and templates need patching, testing, versioning, and retirement. If old images remain available indefinitely, self-service can create workloads that are technically compliant with the automation platform but operationally unsupported or vulnerable. Define who owns image currency and how consumers are moved to newer versions.

Use approval sparingly and intentionally. Manual approvals can provide control for high-risk changes, but overusing them turns self-service into a disguised ticket queue. Prefer policy that can make deterministic decisions automatically, reserving human approval for cases where judgment or external accountability is genuinely required.

For final review, draw one request from catalog click to deployed resource and label every decision point: entitlement, policy, placement, template processing, provider authentication, network and storage selection, extensibility, configuration, and lifecycle. Then repeat the exercise for a failure at each stage. If you can predict where evidence will appear, you understand the automation platform as a system.

Change review should include generated infrastructure as well as automation code. A small template edit can affect many future deployments, while a policy change can alter placement or approvals across projects. Treat those changes with the same peer review and rollback discipline used for other production platform changes.

Maintain a simple dependency diagram for every service you automate. Mark identity, network, DNS, secrets, provider APIs, and external workflows. When a request fails, that diagram provides a faster starting point than searching the entire platform at once.

Operational documentation should state who owns each integration and how it is supported.