Includes questions types found on actual exam such as drag and drop, simulation, type in, and fill in the blank.
NCP-AI Infrastructure NCP-AII Practice Test 39
Question 380
The implementation team reports that it has completed 'use approved proxy or staging pipeline for NGC resources'. Which evidence should the acceptance reviewer request?
expected private container listing appears only within the permitted scope
scripted resource lookup passes on the target CLI release
successful registry request follows the permitted network path and local authorization passes
scheduled jobs resolve the required NGC resources without missing-command errors
Explanation
Correct Answer
C:
Correct. It specifically tests the outcome of the approved remediation. The failure mechanism was network reachability to NGC endpoints was omitted from tool deployment design; the listed result demonstrates the expected behavior.
Incorrect Answers
A:
“expected private container listing appears only within the permitted scope” addresses authorization scope is different from CLI identity authentication, but the current hardware or infrastructure issue is network reachability to NGC endpoints was omitted from tool deployment design. The option “successful registry request follows the permitted network path and local authorization passes” is more directly tied to the required outcome.
B:
“scripted resource lookup passes on the target CLI release” addresses CLI version assumptions were not validated against scripts, but the current hardware or infrastructure issue is network reachability to NGC endpoints was omitted from tool deployment design. The option “successful registry request follows the permitted network path and local authorization passes” is more directly tied to the required outcome.
D:
“scheduled jobs resolve the required NGC resources without missing-command errors” addresses host tool availability does not match the execution placement model, but the current hardware or infrastructure issue is network reachability to NGC endpoints was omitted from tool deployment design. The option “successful registry request follows the permitted network path and local authorization passes” is more directly tied to the required outcome.
Question 381
An NVIDIA infrastructure team reports this operational finding: an infrastructure script pulls the latest mutable NGC container tag every time and job behavior changes unexpectedly. Which action MOST directly addresses it?
use approved secret storage and rotate the exposed NGC token
verify digest or supported manifest signatures for NGC artifacts
pin NGC resource version and retain immutable content identifiers
resolve the approved version or digest explicitly in the NGC retrieval workflow
Explanation
Correct Answer
D:
Correct. The incident follows from mutable artifact tags undermine stable release reproducibility. This action changes the failing dependency, with successive deployments use the identical tracked container manifest as a measurable success criterion.
Incorrect Answers
A:
“use approved secret storage and rotate the exposed NGC token” addresses registry credentials are exposed through unsafe local storage, but the current hardware or infrastructure issue is mutable artifact tags undermine stable release reproducibility. The option “resolve the approved version or digest explicitly in the NGC retrieval workflow” is more directly tied to the required outcome.
B:
“verify digest or supported manifest signatures for NGC artifacts” addresses artifact download completeness and provenance were not verified, but the current hardware or infrastructure issue is mutable artifact tags undermine stable release reproducibility. The option “resolve the approved version or digest explicitly in the NGC retrieval workflow” is more directly tied to the required outcome.
C:
Useful for artifact version and digest were not pinned during acquisition, but it does not remove the specific condition: mutable artifact tags undermine stable release reproducibility.
Question 382
An engineer claims the change 'resolve the approved version or digest explicitly in the NGC retrieval workflow' restored readiness. Which measurable test result would substantiate that claim?
successive deployments use the identical tracked container manifest
expected private container listing appears only within the permitted scope
authenticated NGC CLI lists the approved private resources
scripted resource lookup passes on the target CLI release
Explanation
Correct Answer
A:
Correct. It specifically tests the outcome of the approved remediation. The failure mechanism was mutable artifact tags undermine stable release reproducibility; the listed result demonstrates the expected behavior.
Incorrect Answers
B:
“expected private container listing appears only within the permitted scope” addresses authorization scope is different from CLI identity authentication, but the current hardware or infrastructure issue is mutable artifact tags undermine stable release reproducibility. The option “successive deployments use the identical tracked container manifest” is more directly tied to the required outcome.
C:
“authenticated NGC CLI lists the approved private resources” addresses CLI credentials and organization context are not configured correctly, but the current hardware or infrastructure issue is mutable artifact tags undermine stable release reproducibility. The option “successive deployments use the identical tracked container manifest” is more directly tied to the required outcome.
D:
“scripted resource lookup passes on the target CLI release” addresses CLI version assumptions were not validated against scripts, but the current hardware or infrastructure issue is mutable artifact tags undermine stable release reproducibility. The option “successive deployments use the identical tracked container manifest” is more directly tied to the required outcome.
Question 383
A validation report notes: an NGC CLI upgrade removes a previously used option and automation fails on every worker. What is the MOST defensible next action?
pin NGC resource version and retain immutable content identifiers
qualify NGC CLI version and adapt command syntax in a canary
configure secure NGC authentication and intended organization or team scope
select the correct authorized NGC organization and team
Explanation
Correct Answer
B:
Correct. The incident follows from CLI version assumptions were not validated against scripts. This action changes the failing dependency, with scripted resource lookup passes on the target CLI release as a measurable success criterion.
Incorrect Answers
A:
Useful for artifact version and digest were not pinned during acquisition, but it does not remove the specific condition: CLI version assumptions were not validated against scripts.
C:
“configure secure NGC authentication and intended organization or team scope” addresses CLI credentials and organization context are not configured correctly, but the current hardware or infrastructure issue is CLI version assumptions were not validated against scripts. The option “qualify NGC CLI version and adapt command syntax in a canary” is more directly tied to the required outcome.
D:
“select the correct authorized NGC organization and team” addresses authorization scope is different from CLI identity authentication, but the current hardware or infrastructure issue is CLI version assumptions were not validated against scripts. The option “qualify NGC CLI version and adapt command syntax in a canary” is more directly tied to the required outcome.
Question 384
The site has performed 'qualify NGC CLI version and adapt command syntax in a canary'. Which direct result is needed to close this issue rather than merely check a configuration?
authenticated NGC CLI lists the approved private resources
repeated download produces matching artifact checksum and recorded revision
scripted resource lookup passes on the target CLI release
successful registry request follows the permitted network path and local authorization passes
Explanation
Correct Answer
C:
Correct. It specifically tests the outcome of the approved remediation. The failure mechanism was CLI version assumptions were not validated against scripts; the listed result demonstrates the expected behavior.
Incorrect Answers
A:
“authenticated NGC CLI lists the approved private resources” addresses CLI credentials and organization context are not configured correctly, but the current hardware or infrastructure issue is CLI version assumptions were not validated against scripts. The option “scripted resource lookup passes on the target CLI release” is more directly tied to the required outcome.
B:
“repeated download produces matching artifact checksum and recorded revision” addresses artifact version and digest were not pinned during acquisition, but the current hardware or infrastructure issue is CLI version assumptions were not validated against scripts. The option “scripted resource lookup passes on the target CLI release” is more directly tied to the required outcome.
D:
“successful registry request follows the permitted network path and local authorization passes” addresses network reachability to NGC endpoints was omitted from tool deployment design, but the current hardware or infrastructure issue is CLI version assumptions were not validated against scripts. The option “scripted resource lookup passes on the target CLI release” is more directly tied to the required outcome.
Question 385
A node has correct NGC CLI credentials but cannot write downloaded artifacts due to an incorrect cache-directory ownership. Which corrective action is MOST appropriate before commissioning can proceed?
configure secure NGC authentication and intended organization or team scope
configure approved NGC cache path ownership and quota
use approved secret storage and rotate the exposed NGC token
select the correct authorized NGC organization and team
Explanation
Correct Answer
B:
Correct. This is the corrective step for download destination permissions are mismatched to the executing service account; it addresses the failure at its source. The confirming observation is nonprivileged account downloads and validates a resource without permission errors.
Incorrect Answers
A:
“configure secure NGC authentication and intended organization or team scope” addresses CLI credentials and organization context are not configured correctly, but the current hardware or infrastructure issue is download destination permissions are mismatched to the executing service account. The option “configure approved NGC cache path ownership and quota” is more directly tied to the required outcome.
C:
Useful for registry credentials are exposed through unsafe local storage, but it does not remove the specific condition: download destination permissions are mismatched to the executing service account.
D:
“select the correct authorized NGC organization and team” addresses authorization scope is different from CLI identity authentication, but the current hardware or infrastructure issue is download destination permissions are mismatched to the executing service account. The option “configure approved NGC cache path ownership and quota” is more directly tied to the required outcome.
Question 386
During an AI-factory acceptance review, the following condition is observed: a newly delivered HGX worker passes idle monitoring but GPU errors appear when all accelerators reach full utilization. What should the infrastructure engineer do FIRST?
run a qualified all-GPU stress profile with temperature and Xid telemetry
stress the affected GPU and correlate throttle reasons with system telemetry
investigate ECC telemetry and isolate defective memory during a supported stress run
run simultaneous multi-GPU stress at the approved concurrency
Explanation
Correct Answer
A:
Correct. This is the corrective step for idle health checks failed to exercise sustained compute and power load; it addresses the failure at its source. The confirming observation is all accelerators sustain the accepted workload duration without critical errors.
Incorrect Answers
B:
Useful for GPU cooling or power behavior degrades only under sustained compute duty, but it does not remove the specific condition: idle health checks failed to exercise sustained compute and power load.
C:
“investigate ECC telemetry and isolate defective memory during a supported stress run” addresses correct numerical outputs do not negate accumulating hardware memory faults, but the current hardware or infrastructure issue is idle health checks failed to exercise sustained compute and power load. The option “run a qualified all-GPU stress profile with temperature and Xid telemetry” is more directly tied to the required outcome.
D:
“run simultaneous multi-GPU stress at the approved concurrency” addresses aggregate power and thermal behavior across all GPUs was never validated, but the current hardware or infrastructure issue is idle health checks failed to exercise sustained compute and power load. The option “run a qualified all-GPU stress profile with temperature and Xid telemetry” is more directly tied to the required outcome.
Question 387
After applying 'run a qualified all-GPU stress profile with temperature and Xid telemetry' in an NVIDIA AI factory, what would be the MOST relevant operational verification?
all accelerators sustain the accepted workload duration without critical errors
sampled clocks remain above the approved floor without unexpected throttle flags
repeated full-duration stress passes without reboot ECC or power alarms
results trace directly to the installed replacement board
Explanation
Correct Answer
A:
Correct. The test directly checks whether idle health checks failed to exercise sustained compute and power load has been remediated. That result supports operational acceptance rather than only configuration presence.
Incorrect Answers
B:
“sampled clocks remain above the approved floor without unexpected throttle flags” addresses gPU cooling or power behavior degrades only under sustained compute duty, but the current hardware or infrastructure issue is idle health checks failed to exercise sustained compute and power load. The option “all accelerators sustain the accepted workload duration without critical errors” is more directly tied to the required outcome.
C:
“repeated full-duration stress passes without reboot ECC or power alarms” addresses short smoke testing missed a load-dependent power or cooling instability, but the current hardware or infrastructure issue is idle health checks failed to exercise sustained compute and power load. The option “all accelerators sustain the accepted workload duration without critical errors” is more directly tied to the required outcome.
D:
“results trace directly to the installed replacement board” addresses burn-in evidence cannot be attributed to the replaced hardware, but the current hardware or infrastructure issue is idle health checks failed to exercise sustained compute and power load. The option “all accelerators sustain the accepted workload duration without critical errors” is more directly tied to the required outcome.
Question 388
At a GPU cluster site, a node passes a quick CUDA kernel but reboots after fifteen minutes of a memory-intensive workload. Which remediation best targets the underlying cause?
stress the affected GPU and correlate throttle reasons with system telemetry
investigate ECC telemetry and isolate defective memory during a supported stress run
run a qualified all-GPU stress profile with temperature and Xid telemetry
extend bounded single-node compute and memory stress under monitored conditions
Explanation
Correct Answer
D:
Correct. This directly resolves short smoke testing missed a load-dependent power or cooling instability. A valid handover should also demonstrate repeated full-duration stress passes without reboot ECC or power alarms.
Incorrect Answers
A:
“stress the affected GPU and correlate throttle reasons with system telemetry” addresses gPU cooling or power behavior degrades only under sustained compute duty, but the current hardware or infrastructure issue is short smoke testing missed a load-dependent power or cooling instability. The option “extend bounded single-node compute and memory stress under monitored conditions” is more directly tied to the required outcome.
B:
“investigate ECC telemetry and isolate defective memory during a supported stress run” addresses correct numerical outputs do not negate accumulating hardware memory faults, but the current hardware or infrastructure issue is short smoke testing missed a load-dependent power or cooling instability. The option “extend bounded single-node compute and memory stress under monitored conditions” is more directly tied to the required outcome.
C:
“run a qualified all-GPU stress profile with temperature and Xid telemetry” addresses idle health checks failed to exercise sustained compute and power load, but the current hardware or infrastructure issue is short smoke testing missed a load-dependent power or cooling instability. The option “extend bounded single-node compute and memory stress under monitored conditions” is more directly tied to the required outcome.
Question 389
For a controlled change involving 'extend bounded single-node compute and memory stress under monitored conditions', which acceptance evidence would best establish the intended outcome?
repeated full-duration stress passes without reboot ECC or power alarms
sampled clocks remain above the approved floor without unexpected throttle flags
all accelerators sustain the accepted workload duration without critical errors
aggregate load stays inside rack power and device thermal limits
Explanation
Correct Answer
A:
Correct. This is observable proof for the intended change, not just evidence that a command was issued. It establishes repeated full-duration stress passes without reboot ECC or power alarms, addressing short smoke testing missed a load-dependent power or cooling instability.
Incorrect Answers
B:
“sampled clocks remain above the approved floor without unexpected throttle flags” addresses gPU cooling or power behavior degrades only under sustained compute duty, but the current hardware or infrastructure issue is short smoke testing missed a load-dependent power or cooling instability. The option “repeated full-duration stress passes without reboot ECC or power alarms” is more directly tied to the required outcome.
C:
“all accelerators sustain the accepted workload duration without critical errors” addresses idle health checks failed to exercise sustained compute and power load, but the current hardware or infrastructure issue is short smoke testing missed a load-dependent power or cooling instability. The option “repeated full-duration stress passes without reboot ECC or power alarms” is more directly tied to the required outcome.
D:
“aggregate load stays inside rack power and device thermal limits” addresses aggregate power and thermal behavior across all GPUs was never validated, but the current hardware or infrastructure issue is short smoke testing missed a load-dependent power or cooling instability. The option “repeated full-duration stress passes without reboot ECC or power alarms” is more directly tied to the required outcome.
Question 390
During a readiness assessment, an infrastructure engineer tests one GPU at a time and signs off an eight-GPU server without concurrent loading. Which correction should be prioritized?
stress the affected GPU and correlate throttle reasons with system telemetry
run a qualified all-GPU stress profile with temperature and Xid telemetry
run simultaneous multi-GPU stress at the approved concurrency
monitor NIC links storage and GPU telemetry during maximum approved node stress
Explanation
Correct Answer
C:
Correct. The incident follows from aggregate power and thermal behavior across all GPUs was never validated. This action changes the failing dependency, with aggregate load stays inside rack power and device thermal limits as a measurable success criterion.
Incorrect Answers
A:
“stress the affected GPU and correlate throttle reasons with system telemetry” addresses gPU cooling or power behavior degrades only under sustained compute duty, but the current hardware or infrastructure issue is aggregate power and thermal behavior across all GPUs was never validated. The option “run simultaneous multi-GPU stress at the approved concurrency” is more directly tied to the required outcome.
B:
Useful for idle health checks failed to exercise sustained compute and power load, but it does not remove the specific condition: aggregate power and thermal behavior across all GPUs was never validated.
D:
“monitor NIC links storage and GPU telemetry during maximum approved node stress” addresses system-level concurrent device behavior is not included in single-node acceptance, but the current hardware or infrastructure issue is aggregate power and thermal behavior across all GPUs was never validated. The option “run simultaneous multi-GPU stress at the approved concurrency” is more directly tied to the required outcome.
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NVIDIA NCP-AII Practice Test Questions, NVIDIA NCP-AII Exam Practice Test Questions
Looking to pass your tests the first time. You can study with NVIDIA NCP-AII certification practice test questions and answers, study guide, training courses. With Exam-Labs VCE files you can prepare with NVIDIA NCP-AII NCP-AI Infrastructure exam practice test questions and answers. The most complete solution for passing with NVIDIA certification NCP-AII exam practice test questions and answers, study guide, training course.
NCP-AII: NVIDIA AI Infrastructure Professional
NCP-AII is NVIDIA’s professional certification for deploying, configuring, and validating advanced AI infrastructure. The current exam is remotely proctored, lasts 120 minutes, and lists roughly 70–75 questions. NVIDIA recommends two to three years of operational data-center experience with NVIDIA hardware solutions, which is a useful indicator of the expected depth: this is a deployment credential for practitioners, not an introduction to GPU terminology.
The blueprint is heavily weighted toward hands-on infrastructure outcomes. System and Server Bring-up accounts for 31 percent, Physical Layer Management 5 percent, Control Plane Installation and Configuration 19 percent, and Cluster Test and Verification 33 percent, with the remaining objectives focused on troubleshooting and related operational tasks. Candidates need to understand how an AI platform is assembled, proven healthy, and made ready for workloads.
The natural foundation is NCA-AIIO, which explains the broader AI infrastructure model. NCP-AII then goes deeper into implementation. It is complementary to NCP-AIN for the network fabric and NCP-AIO for ongoing cluster operations.
Professional-level infrastructure work starts with controlled server bring-up
The largest implementation domain begins before an operating system is useful. Candidates need to understand deployment sequencing, BMC and out-of-band management, TPM-related setup, firmware, fault detection, power and cooling validation, GPU installation, cable and transceiver checks, and the initial proof that hardware behaves as expected. Each step is a dependency for the next one.
The important habit is to treat bring-up as a validation process rather than an installation checklist. A server that powers on may still have the wrong firmware, an unhealthy accelerator, a cabling problem, or a thermal condition that appears only under load. Professional engineers establish a known baseline before adding cluster software so later failures can be traced to the correct layer.
Bring-up also has a documentation dimension. Serial numbers, component inventory, firmware baselines, cable maps, management addresses, and test results should be captured as the system is built. That record becomes the reference for warranty work, replacement hardware, future expansion, and incident comparison. In a large cluster, undocumented differences between nodes can become operational debt very quickly.
AI factory topology makes physical connectivity part of system design
NVIDIA’s blueprint expects candidates to understand network topologies for AI factories and to validate the physical links that support them. Accelerator clusters depend on predictable east-west communication, and small cabling or transceiver errors can produce large performance effects when collective operations span many nodes.
The networking concepts in Ethernet switching are still relevant, but an AI fabric also introduces high-speed interconnect choices, rail-oriented layouts, RDMA behavior, and strict performance expectations. NCP-AII candidates do not need to replace a specialist network engineer, yet they must know enough to verify that the infrastructure handed to the cluster is suitable.
Control-plane installation creates the software environment that makes hardware usable
The control-plane domain includes installing Base Command Manager, configuring high availability, installing operating systems, building the cluster, configuring interfaces, and installing components such as Slurm, Enroot, Pyxis, GPU drivers, DOCA drivers, the NVIDIA Container Toolkit, and NGC tooling. The challenge is the dependency graph among those components.
Version compatibility is critical. Drivers, firmware, container runtimes, orchestration tools, and workload libraries can all constrain one another. A professional candidate should understand how to consult support matrices, stage an update, validate the result, and avoid creating a cluster where each component is individually installed but the whole stack is unsupported.
Base Command Manager and related control-plane tooling should be understood in terms of function rather than screen location. Candidates need to know how node categories, images, interfaces, provisioning, software distribution, and high-availability controls contribute to consistent cluster state. If one node differs from its peers, the engineer should be able to determine whether the drift came from provisioning, a failed update, or a deliberate exception.
Containers and schedulers connect infrastructure to real AI workloads
Modern AI environments commonly use containers to package software and schedulers to allocate scarce accelerator resources. The exam includes using NVIDIA GPUs with Docker and preparing cluster components such as Slurm and container integrations. Candidates should understand why containers improve reproducibility while still depending on the host driver, runtime configuration, device exposure, networking, and storage.
The broader distinction between containers and virtualized environments helps frame the design choices. A container is not a hardware abstraction that removes every host dependency. For GPU workloads, the relationship among the host, driver, runtime, image, and scheduler has to remain visible during troubleshooting.
Schedulers also expose infrastructure policy. Slurm partitions, resource requests, priorities, and job placement influence which workloads reach which accelerators. Even when NCP-AII is focused on deployment, the engineer must verify that the installed scheduler can see the hardware correctly and that a representative workload can be placed and executed without hidden device or topology problems.
Cluster verification is a performance proof, not a green status light
NVIDIA assigns 33 percent of the blueprint to cluster test and verification. Objectives include single-node stress tests, HPL, NCCL, signal-quality checks, firmware confirmation on switches and BlueField components, ClusterKit assessments, fabric-bandwidth verification, and burn-in testing. This reflects a fundamental services principle: a cluster is not ready merely because it formed successfully.
Candidates should understand what each test proves. HPL stresses floating-point compute and broader system behavior, while NCCL exercises GPU communication paths and can reveal fabric or topology problems. A signal-quality or cable test answers a different question from a software health check. The professional skill is selecting the right test for the failure hypothesis and interpreting the result in context.
Burn-in should be planned so that it stresses the system without obscuring the cause of a failure. Running every possible test simultaneously may produce impressive load while making diagnosis difficult. A better validation sequence establishes single-node health, then intra-node GPU communication, then inter-node fabric behavior, and finally sustained cluster-scale tests. The sequence narrows the point at which a defect appears.
Acceptance testing should also compare the assembled cluster with a declared baseline. The point is not merely to produce one fast benchmark run; it is to prove that nodes behave consistently enough to enter service. Outliers in GPU health, interconnect throughput, thermals, firmware, or storage access deserve investigation even when the aggregate result looks acceptable. A professional handoff should therefore include expected ranges, failed or deferred tests, remediation notes, and a clear statement of which evidence demonstrates that the infrastructure is ready for production workloads.
Power, cooling, and hardware validation remain relevant under sustained load
AI systems can pass an idle inspection and fail during sustained work if power delivery, thermal headroom, or cooling is inadequate. The bring-up objectives therefore include validating facility parameters before burn-in. Candidates should be able to explain why load testing belongs after physical and firmware checks and before declaring the system production-ready.
This is where general data-center design becomes operational. Rack power, airflow, cooling strategy, service clearances, and monitoring all influence the reliability of expensive accelerator systems. Infrastructure teams need a common language with facilities teams because neither side can validate the deployment alone.
Troubleshooting should preserve evidence across hardware and software layers
Professional AI infrastructure failures rarely announce which layer is responsible. A workload error can originate in a GPU, transceiver, switch, firmware mismatch, driver, container runtime, scheduler, storage system, or application. NCP-AII preparation should therefore practice evidence collection from the lowest physical layer through the control plane before making changes.
A disciplined process begins with scope and recent change. Compare affected and healthy nodes, inspect hardware telemetry, verify versions, test the fabric, reproduce the failure with a known workload, and make one controlled correction. If the problem is escalated, the collected evidence should allow another engineer to continue from the same point rather than restart diagnosis.
Storage deserves explicit attention because training and validation tools can themselves create heavy I/O. If compute and fabric tests pass but workloads stall while loading datasets or writing checkpoints, the storage path may be the limiting layer. Candidates should know how to distinguish a compute-bound test from an I/O-bound workload before concluding that the GPUs or network are underperforming.
A useful isolation technique is to change one dimension at a time. If the same workload fails only on one node, compare that node with a healthy peer before changing cluster-wide configuration. If several nodes fail across the same fabric path, shift attention toward switching, cabling, transceivers, or collective-communication behavior. If failures begin immediately after a driver or image change, test the version boundary before replacing hardware. This controlled comparison prevents expensive component swaps from becoming substitutes for diagnosis and makes the final root-cause record defensible.
Study should culminate in a documented build-and-validate runbook
A good NCP-AII lab starts with a blank or reset environment and ends with a cluster that has passed defined acceptance tests. Record BMC settings, firmware, network interfaces, driver versions, container runtime, scheduler configuration, and validation results. Then deliberately break one dependency and demonstrate that the runbook helps locate the problem.
Use NVIDIA documentation and the current exam blueprint as the authority for supported procedures. The strongest preparation does not merely memorize the names of HPL, NCCL, BCM, or NGC; it explains why each belongs in the deployment lifecycle, what evidence it produces, and what decision an engineer can make from that evidence.
For final review, walk through the build in dependency order and state the acceptance criterion for each layer. If you cannot say what “good” looks like for firmware, cabling, drivers, fabric, scheduler, and stress testing, the corresponding section deserves another lab pass.
Candidates should also rehearse the handoff from commissioning to operations: what monitoring remains active, which baselines are retained, how configuration drift is detected, and what evidence an operations team needs when a node later departs from the accepted state.
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