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Record offline control-plane limits in the availability plan. This option addresses a different situation (cloud control-plane dependency), in which remote workloads are expected to continue local operations even when the cloud management console is temporarily unreachable. It is not the best response here because network transfer time and contention can dominate remote data service response independently of storage media speed. The relevant diagnostic or acceptance evidence in this case concerns: link graphs show saturation and increased application delays during imaging uploads.

Revalidate sizing after major application or concurrency changes. This option addresses a different situation (peak memory working set versus CPU utilization), in which a VM migration is sized only from average CPU figures although the applications are paging heavily at the business peak. It is not the best response here because network transfer time and contention can dominate remote data service response independently of storage media speed. The relevant diagnostic or acceptance evidence in this case concerns: link graphs show saturation and increased application delays during imaging uploads.

Run negative cross-tenant access tests after each policy change. This option addresses a different situation (multi-tenant blast radius), in which unrelated customers share management resources without clear tenant scoping for administrative actions. It is not the best response here because network transfer time and contention can dominate remote data service response independently of storage media speed. The relevant diagnostic or acceptance evidence in this case concerns: link graphs show saturation and increased application delays during imaging uploads.

Test application behavior with the WAN disconnected and measure maximum tolerable decision latency. This option addresses a different situation (edge sensor inference under outage), in which an automated manufacturing line must perform safety-related inference even when its WAN link is unavailable. It is not the best response here because inconsistent standards make ownership, chargeback and compliance controls hard to enforce across cloud boundaries. The relevant diagnostic or acceptance evidence in this case concerns: an estate inventory reveals untagged resources and conflicting retention policies.

Review colocation contract boundaries for power, cross-connects, physical access and managed services. This option addresses a different situation (colocation managed custody), in which a customer needs hardware in a regional colocation facility and wants to distinguish hosting from operations responsibility. It is not the best response here because inconsistent standards make ownership, chargeback and compliance controls hard to enforce across cloud boundaries. The relevant diagnostic or acceptance evidence in this case concerns: an estate inventory reveals untagged resources and conflicting retention policies.

Map correlated failure domains, physical utilities, connectivity, and geographic hazards. This option addresses a different situation (service availability zone semantics), in which the team calls two racks in the same flood-prone building separate disaster recovery sites. It is not the best response here because inconsistent standards make ownership, chargeback and compliance controls hard to enforce across cloud boundaries. The relevant diagnostic or acceptance evidence in this case concerns: an estate inventory reveals untagged resources and conflicting retention policies.

A virtualization layer provides consolidation but may impose compatibility or resource-scheduling constraints for a specific workload. This option addresses a different situation (virtual machine versus bare metal), in which a licensed latency-sensitive workload needs direct accelerator and driver access while the default design assumes every service must be a VM. It is not the best response here because inconsistent standards make ownership, chargeback and compliance controls hard to enforce across cloud boundaries. The relevant diagnostic or acceptance evidence in this case concerns: an estate inventory reveals untagged resources and conflicting retention policies.

Container packaging does not automatically remove monolithic statefulness or architecture coupling. This option addresses a different situation (cloud-native versus lift-and-shift), in which a legacy monolith is proposed for containers but has hardcoded node identities and writes session state to local disks. It is not the best response here because inconsistent standards make ownership, chargeback and compliance controls hard to enforce across cloud boundaries. The relevant diagnostic or acceptance evidence in this case concerns: an estate inventory reveals untagged resources and conflicting retention policies.

A cloud-only inference dependency cannot meet an offline edge decision requirement when connectivity fails. This option addresses a different situation (edge sensor inference under outage), in which an automated manufacturing line must perform safety-related inference even when its WAN link is unavailable. It is not the best response here because inconsistent standards make ownership, chargeback and compliance controls hard to enforce across cloud boundaries. The relevant diagnostic or acceptance evidence in this case concerns: an estate inventory reveals untagged resources and conflicting retention policies.

A line-item model shows omitted operating and network expenses change relative economics. This option addresses a different situation (cost comparison unit mismatch), in which a steering committee compares the monthly price of a cloud VM against an on-prem server purchase without support, facilities, licences or operations. It is not the best response here because inconsistent standards make ownership, chargeback and compliance controls hard to enforce across cloud boundaries. The relevant diagnostic or acceptance evidence in this case concerns: an estate inventory reveals untagged resources and conflicting retention policies.

An isolated recovery exercise restores data from a separately protected copy after production credentials are disabled. This option addresses a different situation (immutable cyber recovery copy), in which an organization relies on writable live snapshots alone as its ransomware recovery strategy. It is not the best response here because inconsistent standards make ownership, chargeback and compliance controls hard to enforce across cloud boundaries. The relevant diagnostic or acceptance evidence in this case concerns: an estate inventory reveals untagged resources and conflicting retention policies.

Vendor support and a representative benchmark show whether the VM profile meets the target. This option addresses a different situation (virtual machine versus bare metal), in which a licensed latency-sensitive workload needs direct accelerator and driver access while the default design assumes every service must be a VM. It is not the best response here because inconsistent standards make ownership, chargeback and compliance controls hard to enforce across cloud boundaries. The relevant diagnostic or acceptance evidence in this case concerns: an estate inventory reveals untagged resources and conflicting retention policies.

Size host memory and compute using peak workload working sets and acceptable headroom. This option addresses a different situation (peak memory working set versus CPU utilization), in which a VM migration is sized only from average CPU figures although the applications are paging heavily at the business peak. It is not the best response here because inconsistent standards make ownership, chargeback and compliance controls hard to enforce across cloud boundaries. The relevant diagnostic or acceptance evidence in this case concerns: an estate inventory reveals untagged resources and conflicting retention policies.

Choose bare-metal or supported virtualization per workload rather than forcing a single compute model. This option addresses a different situation (virtual machine versus bare metal), in which a licensed latency-sensitive workload needs direct accelerator and driver access while the default design assumes every service must be a VM. It is not the best response here because inconsistent standards make ownership, chargeback and compliance controls hard to enforce across cloud boundaries. The relevant diagnostic or acceptance evidence in this case concerns: an estate inventory reveals untagged resources and conflicting retention policies.

Evaluate placing compute near the data or keeping the stable data-intensive workload in a private environment. This option addresses a different situation (cloud egress cost of analytics), in which a reporting application repeatedly reads several terabytes of private data back from a public-cloud location to an on-site analytics cluster. It is not the best response here because inconsistent standards make ownership, chargeback and compliance controls hard to enforce across cloud boundaries. The relevant diagnostic or acceptance evidence in this case concerns: an estate inventory reveals untagged resources and conflicting retention policies.

Test disabled accounts, least privilege, and audit trails across environments. This option addresses a different situation (hybrid identity portability), in which a company wants users to move between private and public workloads while maintaining access governance. It is not the best response here because inconsistent standards make ownership, chargeback and compliance controls hard to enforce across cloud boundaries. The relevant diagnostic or acceptance evidence in this case concerns: an estate inventory reveals untagged resources and conflicting retention policies.

Record offline control-plane limits in the availability plan. This option addresses a different situation (cloud control-plane dependency), in which remote workloads are expected to continue local operations even when the cloud management console is temporarily unreachable. It is not the best response here because inconsistent standards make ownership, chargeback and compliance controls hard to enforce across cloud boundaries. The relevant diagnostic or acceptance evidence in this case concerns: an estate inventory reveals untagged resources and conflicting retention policies.

Review the responsibility matrix whenever services or deployment models change. This option addresses a different situation (shared responsibility confusion), in which a client expects a cloud consumption contract to relieve it automatically of application configuration and data classification duties. It is not the best response here because inconsistent standards make ownership, chargeback and compliance controls hard to enforce across cloud boundaries. The relevant diagnostic or acceptance evidence in this case concerns: an estate inventory reveals untagged resources and conflicting retention policies.

Identify operating-system, state, and service-discovery assumptions before selecting a target runtime. This option addresses a different situation (cloud-native versus lift-and-shift), in which a legacy monolith is proposed for containers but has hardcoded node identities and writes session state to local disks. It is not the best response here because AI compute location and software controls determine whether sensitive training data crosses a prohibited boundary. The relevant diagnostic or acceptance evidence in this case concerns: a data-flow review confirms training and inference inputs remain in the approved environment.

Test application behavior with the WAN disconnected and measure maximum tolerable decision latency. This option addresses a different situation (edge sensor inference under outage), in which an automated manufacturing line must perform safety-related inference even when its WAN link is unavailable. It is not the best response here because AI compute location and software controls determine whether sensitive training data crosses a prohibited boundary. The relevant diagnostic or acceptance evidence in this case concerns: a data-flow review confirms training and inference inputs remain in the approved environment.

Collect utilization by hour and compare burst duration with always-on owned capacity. This option addresses a different situation (bursty short-lived rendering workload), in which a media team needs hundreds of additional compute instances for a few hours each month but little extra capacity the rest of the time. It is not the best response here because AI compute location and software controls determine whether sensitive training data crosses a prohibited boundary. The relevant diagnostic or acceptance evidence in this case concerns: a data-flow review confirms training and inference inputs remain in the approved environment.

A virtualization layer provides consolidation but may impose compatibility or resource-scheduling constraints for a specific workload. This option addresses a different situation (virtual machine versus bare metal), in which a licensed latency-sensitive workload needs direct accelerator and driver access while the default design assumes every service must be a VM. It is not the best response here because AI compute location and software controls determine whether sensitive training data crosses a prohibited boundary. The relevant diagnostic or acceptance evidence in this case concerns: a data-flow review confirms training and inference inputs remain in the approved environment.

Container packaging does not automatically remove monolithic statefulness or architecture coupling. This option addresses a different situation (cloud-native versus lift-and-shift), in which a legacy monolith is proposed for containers but has hardcoded node identities and writes session state to local disks. It is not the best response here because AI compute location and software controls determine whether sensitive training data crosses a prohibited boundary. The relevant diagnostic or acceptance evidence in this case concerns: a data-flow review confirms training and inference inputs remain in the approved environment.

A cloud-only inference dependency cannot meet an offline edge decision requirement when connectivity fails. This option addresses a different situation (edge sensor inference under outage), in which an automated manufacturing line must perform safety-related inference even when its WAN link is unavailable. It is not the best response here because AI compute location and software controls determine whether sensitive training data crosses a prohibited boundary. The relevant diagnostic or acceptance evidence in this case concerns: a data-flow review confirms training and inference inputs remain in the approved environment.

A user from one tenant is denied access to another tenant's protected resources in test. This option addresses a different situation (multi-tenant blast radius), in which unrelated customers share management resources without clear tenant scoping for administrative actions. It is not the best response here because AI compute location and software controls determine whether sensitive training data crosses a prohibited boundary. The relevant diagnostic or acceptance evidence in this case concerns: a data-flow review confirms training and inference inputs remain in the approved environment.

A line-item model shows omitted operating and network expenses change relative economics. This option addresses a different situation (cost comparison unit mismatch), in which a steering committee compares the monthly price of a cloud VM against an on-prem server purchase without support, facilities, licences or operations. It is not the best response here because AI compute location and software controls determine whether sensitive training data crosses a prohibited boundary. The relevant diagnostic or acceptance evidence in this case concerns: a data-flow review confirms training and inference inputs remain in the approved environment.

An isolated recovery exercise restores data from a separately protected copy after production credentials are disabled. This option addresses a different situation (immutable cyber recovery copy), in which an organization relies on writable live snapshots alone as its ransomware recovery strategy. It is not the best response here because AI compute location and software controls determine whether sensitive training data crosses a prohibited boundary. The relevant diagnostic or acceptance evidence in this case concerns: a data-flow review confirms training and inference inputs remain in the approved environment.

Design local autonomy and recovery procedures for the exact control-plane failure case. This option addresses a different situation (cloud control-plane dependency), in which remote workloads are expected to continue local operations even when the cloud management console is temporarily unreachable. It is not the best response here because AI compute location and software controls determine whether sensitive training data crosses a prohibited boundary. The relevant diagnostic or acceptance evidence in this case concerns: a data-flow review confirms training and inference inputs remain in the approved environment.

Define tenant resource boundaries and least-privilege role assignments. This option addresses a different situation (multi-tenant blast radius), in which unrelated customers share management resources without clear tenant scoping for administrative actions. It is not the best response here because AI compute location and software controls determine whether sensitive training data crosses a prohibited boundary. The relevant diagnostic or acceptance evidence in this case concerns: a data-flow review confirms training and inference inputs remain in the approved environment.

Prepare a consistent multiyear TCO and cash-flow model with explicit assumptions. This option addresses a different situation (cost comparison unit mismatch), in which a steering committee compares the monthly price of a cloud VM against an on-prem server purchase without support, facilities, licences or operations. It is not the best response here because AI compute location and software controls determine whether sensitive training data crosses a prohibited boundary. The relevant diagnostic or acceptance evidence in this case concerns: a data-flow review confirms training and inference inputs remain in the approved environment.

Audit role-mapping policies and test privileged resource access regularly. This option addresses a different situation (zero-trust versus trusted network premise), in which a new employee receives broad application access merely because their laptop connects to the corporate Wi-Fi. It is not the best response here because AI compute location and software controls determine whether sensitive training data crosses a prohibited boundary. The relevant diagnostic or acceptance evidence in this case concerns: a data-flow review confirms training and inference inputs remain in the approved environment.

Test disabled accounts, least privilege, and audit trails across environments. This option addresses a different situation (hybrid identity portability), in which a company wants users to move between private and public workloads while maintaining access governance. It is not the best response here because AI compute location and software controls determine whether sensitive training data crosses a prohibited boundary. The relevant diagnostic or acceptance evidence in this case concerns: a data-flow review confirms training and inference inputs remain in the approved environment.

Record offline control-plane limits in the availability plan. This option addresses a different situation (cloud control-plane dependency), in which remote workloads are expected to continue local operations even when the cloud management console is temporarily unreachable. It is not the best response here because AI compute location and software controls determine whether sensitive training data crosses a prohibited boundary. The relevant diagnostic or acceptance evidence in this case concerns: a data-flow review confirms training and inference inputs remain in the approved environment.