Microsoft AZ-104 Exam Dumps, Practice Test Questions

An instance may be running while its application returns errors. Instance count alone cannot identify application-specific failures.

ProvisioningState reports the management-plane deployment state, not the health of the running HTTP API. An instance can finish provisioning while its process is down or returns failing responses. Automatic upgrades and repairs require a supported application health signal or probe aligned with the actual backend behavior, not merely a provisioning-success value.

Power or instance provisioning state does not establish that an HTTP listener answers requests correctly. A VM can be running while its API fails health checks. VMSS upgrade and repair decisions need a supported application health signal such as the Application Health extension rather than treating VM running state as application health.

Without a relevant automatic scale rule, the desired metric-driven behavior may stop entirely rather than become stable.

Changing metric sampling can alter signal smoothing but does not impose the explicit post-scale waiting period the operator requested. The cooldown property controls rule eligibility after a scale action.

A higher maximum does not add a pause between autoscale operations. The existing rules can continue changing capacity frequently when the threshold oscillates.

Stopping the entire set removes serving capacity and abandons the stated staggered-change requirement.

Cooldown delays metric-triggered instance count adjustments, not necessarily the rollout of a new VMSS image or model. It does not establish a rolling upgrade batch policy.

Automatic upgrade may apply a new model, but without the desired rolling and health safeguards it does not satisfy controlled batches with retained healthy capacity.

Orchestration mode is not derived dynamically from current capacity. Instance-count changes do not convert the scale set’s management model.

Deallocation affects current instance execution, not the immutable orchestration mode selected at VMSS creation. The update remains unsupported.

Reimaging resets instance OS state from a selected image but does not convert the scale set’s orchestration model or instance resource API.

Uniform is optimized for large sets of similar VMs with scale-set-specific instance APIs. It is not the best fit for full regular VM API management and mixed VM types.

An availability set can improve fault and maintenance separation but is not a scale-set orchestration model with native managed instance scaling across mixed types.

Uniform orchestration mainly uses scale-set-specific instance APIs and an instance profile. It does not provide the same ordinary VM management model as Flexible.

Manual intervention is fragile and removes automatic response to variation during each period. Autoscale can express time-varying profiles.

Disk capacity does not represent the necessary worker count. A single fixed baseline ignores the predictable time-of-day capacity requirement.

Automatic VMSS upgrade settings concern applying changes from a scale-set model to instances, not time-of-day min/default/max autoscale capacity. A scheduled autoscale profile is still required for the requested weekday baseline, while CPU metric rules can remain active within that profile.

The image registry is not the controller that applies a new Azure VM scale-set model to existing compute instances.

That is contrary to Manual policy semantics and could leave unpatched instances if the operator does nothing.

Cooldown affects scaling decisions rather than the explicit instance model upgrade needed under Manual upgrades.

Increasing the minimum fleet size may add spare serving capacity but does not stop each newly created instance from being classified for repair before its own application finishes initializing. The unexpected repair loop arises from a health-evaluation timing assumption, not an insufficient desired capacity setting.

Autoscale cooldown delays subsequent capacity scaling decisions; it is separate from VM scale set automatic instance repair timing. The case describes replacement of new instances due to expected application startup, so adjusting only a scale-out cooldown would not guarantee a startup grace window for the repair controller.

Turning off repairs may suppress the immediate recycling symptom but disables the mechanism needed to recover genuinely unhealthy instances. Autoscale changes capacity based on scaling rules and is not a drop-in replacement for application-health-based repair. The requirement is to retain repairs but avoid mistaking normal boot-time initialization for sustained failure.

Metric-based scaling is a core VMSS capability. The issue is the configured signal’s calculated value, not the absence of the feature.

Autoscale configuration explicitly includes metric evaluation and time windows. Ignoring those controls misdiagnoses a normal nontrigger.

NSGs govern network traffic, not CPU metric threshold computation in Azure Monitor Autoscale.

Protection keeps selected instances from being removed, rather than selecting an ordered list of victims. Protecting every instance can prevent the intended scale-in entirely; the scale-in policy determines removal order among eligible instances.

An autoscale metric threshold determines when and how much capacity to remove, not which VM IDs to choose. The scenario asks specifically for member selection; configure a supported scale-in policy and plan state/cache draining.

A health probe helps assess backend responsiveness but is not an explicit VMSS scale-in rule choosing oldest versus newest eligible members. Configure the scale-in policy for removal order, accounting for protection and zone/fault-domain balance.

A scale-in policy establishes the scale set's general ordering for selecting removal candidates; it does not guarantee protection for a named instance. An instance's age relative to other members can change, so a Newest ordering cannot enforce the explicit temporary exclusion needed for export instance 7.

Protect from scale-set actions is an instance-level feature with broader effects on scale-set operations, not a single switch on the whole scale-set model. It could block legitimate scale-set initiated upgrades or reimages of the protected instance and is unnecessary for the narrower scale-in-only requirement.

Cooldown controls how quickly autoscale repeats scaling actions after a rule fires; it cannot express an exception for a particular instance. Eventually the scale set can still choose instance 7 if the workload contracts, because no instance-level removal protection was configured.