Microsoft Azure SAP AZ-120 Practice Test Questions, Microsoft Azure SAP AZ-120 Exam Practice Test Questions

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Microsoft AZ-120: Planning and Administering Azure for SAP Workloads

Microsoft exam AZ-120, Planning and Administering Azure for SAP Workloads, is current as of October 1, 2026. Microsoft updated the English study guide on April 17, 2026, and the associated Azure for SAP Workloads Specialty remains focused on architects and engineers who plan migrations, implement infrastructure, design high availability and disaster recovery, and operate SAP systems on Azure.

This is not a general Azure exam with a few SAP terms added. Candidates are expected to understand SAP HANA, SAP Business Suite, SAP NetWeaver, database behavior, storage performance, availability architecture, backup, networking, and Azure operations. Microsoft also strongly recommends Azure Administrator-level experience, making AZ-104 an important practical foundation.

The difficulty comes from having to reason across two technical worlds at once. A design can be valid from an Azure infrastructure perspective yet inappropriate for SAP supportability, sizing, latency, or recovery requirements. Effective preparation therefore connects SAP workload characteristics to Azure architecture instead of studying the platforms separately.

SAP migration planning begins with workload evidence rather than cloud enthusiasm

A migration plan should start with the current SAP landscape: application tiers, databases, interfaces, dependencies, data volumes, growth, maintenance windows, business criticality, and performance history. Candidates should know why discovery and sizing are essential before selecting VM families, storage, regions, or migration methods.

The SAP workloads on Azure context is useful because the cloud destination must preserve the operational characteristics that matter to SAP. Moving a system without understanding peak memory, I/O, batch windows, and inter-system traffic can create an expensive environment that still fails business expectations.

Compute selection is constrained by SAP certification, memory, and workload shape

SAP systems can be unusually demanding in memory, CPU, and storage throughput, particularly for HANA. Candidates should understand that Azure VM selection is not merely a matter of choosing a large size. Supported configurations, certified instance types, NUMA behavior, memory ratios, scale-up versus scale-out patterns, and proximity to dependent components can influence the design.

Practice should focus on explaining why a workload fits a selected compute pattern. An application server tier may scale horizontally, while a database tier may have stricter certification and memory requirements. Large environments may also require specialized infrastructure. The exam rewards reasoning about workload characteristics more than memorizing every available VM SKU.

Storage architecture affects both SAP performance and recovery behavior

SAP databases generate data, log, backup, and temporary I/O patterns with different latency and throughput needs. Azure managed disks, shared storage choices, caching, striping, redundancy, and backup targets need to be aligned with those patterns. A design that maximizes raw capacity but ignores latency can perform poorly during business-critical transactions.

Administrators should also separate performance design from protection design. Snapshots, database-native backups, Azure Backup, and replication mechanisms serve different purposes. Recovery point objectives and recovery time objectives determine what combination is appropriate, while retention and restore testing determine whether the plan can actually be used during an incident.

High availability and disaster recovery must be designed across SAP layers

An SAP solution includes more than a database. Application servers, central services, storage, networking, and database components may each need a different availability mechanism. Availability zones, clustering, database replication, load balancing, and recovery-region design should be coordinated so that one protected layer does not hide a single point of failure elsewhere.

The disaster-recovery planning discipline is especially relevant because a technically replicated environment is not automatically recoverable. Runbooks, dependency order, DNS or routing changes, credentials, testing, and operational ownership are part of a complete recovery design.

Networking decisions can determine whether SAP feels local or distant

SAP landscapes often span application servers, databases, user networks, integration systems, and on-premises services. Candidates should understand virtual networks, subnets, routing, name resolution, private connectivity, ExpressRoute, VPN concepts, firewalls, and network-security controls well enough to preserve low-latency communication where the workload requires it.

Large migrations also need a data-transfer strategy. Connectivity capacity, migration windows, synchronization traffic, and cutover plans must be evaluated together. A network that is adequate for steady-state transactions may be insufficient for bulk database transfer, replication, or backup seeding. This is where AZ-120 becomes architecture rather than configuration memorization.

Identity and access must respect both Azure control planes and SAP administration

Azure RBAC controls who can manage resources, while SAP has its own authorization model inside the workload. Candidates should not blur those layers. The cloud platform must restrict who can modify infrastructure, networks, storage, backup, and monitoring, while SAP roles govern application-level business functions and system administration.

Azure RBAC provides the cloud-side least-privilege framework. In a production SAP environment, privileged access should be narrowly scoped, auditable, and separated across duties so that one administrator does not automatically control every infrastructure and application layer.

Monitoring an SAP workload requires correlating Azure and SAP signals

Azure metrics may show CPU, disk latency, network throughput, and platform health, while SAP tools expose application queues, work processes, database behavior, and transaction performance. An incident may appear first in one layer and originate in another. Candidates should therefore learn to correlate evidence rather than troubleshoot Azure and SAP independently.

Azure monitoring provides the infrastructure perspective, but operational readiness also requires thresholds, alert ownership, escalation paths, and baseline knowledge. Teams need to know what normal looks like before they can identify a meaningful deviation during a performance event.

Maintenance work must account for SAP supportability and business windows

Patching, resizing, backup operations, operating-system changes, database maintenance, and Azure platform updates all need controlled processes. SAP environments often have strict availability expectations and tightly managed change windows, so automation should be paired with validation rather than used simply to make changes faster.

A strong candidate can describe a maintenance sequence: verify prerequisites and support statements, confirm recovery options, coordinate stakeholders, apply the change to a controlled scope, validate SAP functionality and performance, and monitor after completion. This operational discipline is one reason AZ-120 expects prior Azure administration experience.

The best study plan uses architecture diagrams and failure scenarios

Draw several SAP-on-Azure designs and annotate where data, identity, network traffic, backups, monitoring, and failover flow. Then change one requirement at a time: tighter recovery objectives, a larger HANA database, cross-region continuity, private connectivity, or a migration with limited downtime. Reworking the architecture reveals which decisions are coupled.

The SAP workloads on Azure for AZ-120 becomes most useful when it supports this scenario work rather than replacing it. The exam tests whether candidates can operate at the boundary between SAP requirements and Azure capabilities, so preparation should repeatedly ask what the workload needs, what Azure provides, and what evidence proves the design is supportable.

Sizing conversations should include growth and operational headroom, not only today’s measured demand. SAP systems may experience month-end processing, seasonal spikes, reporting bursts, or future data growth that does not appear in an average utilization snapshot. An architect should understand the difference between designing for peak sustained demand, temporary burst, failover capacity, and future expansion. These distinctions influence both technical fit and cost.

Migration strategy also depends on the acceptable outage window. Offline copy methods can be simpler but may not meet business requirements for large databases. Replication-based methods reduce downtime but add synchronization, validation, and cutover complexity. Candidates should understand that the network transfer rate, data-change rate, rehearsal quality, and rollback plan all contribute to whether the migration can finish inside the agreed window.

SAP HANA introduces memory and persistence considerations that make generic database assumptions risky. Memory capacity, data and log volumes, backup throughput, and restart behavior all influence the Azure design. Candidates do not need to memorize every certified configuration, but they should know that SAP support matrices and Microsoft guidance constrain the set of acceptable infrastructure choices. Unsupported combinations are not rescued by otherwise elegant architecture.

High availability should also be tested during maintenance scenarios, not only disaster scenarios. Patching one node, restarting a service, or replacing a VM should not create an unexpected full outage if the architecture claims redundancy. Rehearsing planned failover is often the safest way to discover hidden dependencies such as hard-coded names, single-instance central services, or storage paths that were never designed for node movement.

Operational handoff matters because SAP on Azure is usually supported by several teams. Cloud administrators may own subscriptions and network controls, database specialists own HANA or another database, SAP Basis teams own application operations, and security teams govern access. A useful design documents ownership boundaries, escalation paths, maintenance responsibilities, and monitoring responsibilities so incidents do not stall while teams argue about who should act.

Cost optimization should be workload-aware. Reserved capacity, right-sizing, storage-tier decisions, nonproduction shutdown, and backup retention can reduce spend, but aggressive optimization can conflict with performance or recovery objectives. The architect should know which resources are continuously required for SAP service levels and which can be scheduled or scaled. The goal is sustainable cost without making production behavior unpredictable.

Backup strategy should include application-consistent recovery. Copying disks is not enough if the recovered SAP database and application state cannot be brought up cleanly. Database-native backup tools, Azure services, and SAP procedures need to be coordinated so restores are supported and testable. Candidates should understand that backup frequency, log handling, retention, and off-region protection are decisions connected to the stated recovery objectives.

Automation is useful for repeatable provisioning, but SAP environments need carefully controlled parameters. Infrastructure as code can standardize networks, VMs, disks, diagnostics, and policy, yet the templates should reflect certified sizes and operational conventions rather than blindly clone one environment. The best automation reduces drift while preserving the ability to review workload-specific values before deployment.

Before declaring an SAP design ready, verify the support boundary. Confirm that the selected operating system, database, Azure infrastructure, high-availability method, and backup approach are supported together for the SAP workload in question. This habit protects candidates from choosing a technically plausible but operationally unsupported combination.

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