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 22
Question 210
During a readiness assessment, a cluster mounts the same storage export through different paths on each worker and ranks cannot find shared checkpoints. Which correction should be prioritized?
standardize mount paths and validated namespace settings across nodes
align approved NFS export permissions and mount options with least privilege
choose and validate supported consistency and cache invalidation settings
align initiator and target transport addresses with their intended network
Explanation
Correct Answer
A:
Correct. This is the corrective step for filesystem mount namespace and path consistency were not established; it addresses the failure at its source. The confirming observation is all workers resolve the same checkpoint URI and verify matching content hashes.
Incorrect Answers
B:
Useful for export and client mount permissions contradict the checkpoint workload, but it does not remove the specific condition: filesystem mount namespace and path consistency were not established.
C:
“choose and validate supported consistency and cache invalidation settings” addresses storage cache coherency is unsuitable for the application's update model, but the current hardware or infrastructure issue is filesystem mount namespace and path consistency were not established. The option “standardize mount paths and validated namespace settings across nodes” is more directly tied to the required outcome.
D:
“align initiator and target transport addresses with their intended network” addresses fabric endpoint addressing does not match the approved storage path, but the current hardware or infrastructure issue is filesystem mount namespace and path consistency were not established. The option “standardize mount paths and validated namespace settings across nodes” is more directly tied to the required outcome.
Question 211
A team implements the correction 'standardize mount paths and validated namespace settings across nodes' for a GPU cluster problem. Which observed result MOST convincingly proves it worked?
a non-root training job writes and restores a test checkpoint correctly
initiator discovery and sustained storage I/O work over the authorized path
all workers resolve the same checkpoint URI and verify matching content hashes
recovered model and dataset hashes match the protected point-in-time copies
Explanation
Correct Answer
C:
Correct. This is observable proof for the intended change, not just evidence that a command was issued. It establishes all workers resolve the same checkpoint URI and verify matching content hashes, addressing filesystem mount namespace and path consistency were not established.
Incorrect Answers
A:
“a non-root training job writes and restores a test checkpoint correctly” addresses export and client mount permissions contradict the checkpoint workload, but the current hardware or infrastructure issue is filesystem mount namespace and path consistency were not established. The option “all workers resolve the same checkpoint URI and verify matching content hashes” is more directly tied to the required outcome.
B:
“initiator discovery and sustained storage I/O work over the authorized path” addresses fabric endpoint addressing does not match the approved storage path, but the current hardware or infrastructure issue is filesystem mount namespace and path consistency were not established. The option “all workers resolve the same checkpoint URI and verify matching content hashes” is more directly tied to the required outcome.
D:
“recovered model and dataset hashes match the protected point-in-time copies” addresses backup configuration was confused with verified data recoverability, but the current hardware or infrastructure issue is filesystem mount namespace and path consistency were not established. The option “all workers resolve the same checkpoint URI and verify matching content hashes” is more directly tied to the required outcome.
Question 212
During an AI-factory acceptance review, the following condition is observed: a new AI file system advertises high sequential bandwidth but has severe delays when reading millions of tiny data files. What should the infrastructure engineer do FIRST?
apply tenant-scoped credentials and server-side file access rules
benchmark and tune metadata handling for the actual file-size distribution
choose and validate supported consistency and cache invalidation settings
align initiator and target transport addresses with their intended network
Explanation
Correct Answer
B:
Correct. The underlying problem is metadata-operation capability is mismatched to the training data access pattern. This intervention targets that mechanism. Confirm completion through measured file-open rate and training dataloader throughput meet agreed targets.
Incorrect Answers
A:
“apply tenant-scoped credentials and server-side file access rules” addresses storage identity and access isolation are absent, but the current hardware or infrastructure issue is metadata-operation capability is mismatched to the training data access pattern. The option “benchmark and tune metadata handling for the actual file-size distribution” is more directly tied to the required outcome.
C:
“choose and validate supported consistency and cache invalidation settings” addresses storage cache coherency is unsuitable for the application's update model, but the current hardware or infrastructure issue is metadata-operation capability is mismatched to the training data access pattern. The option “benchmark and tune metadata handling for the actual file-size distribution” is more directly tied to the required outcome.
D:
Useful for fabric endpoint addressing does not match the approved storage path, but it does not remove the specific condition: metadata-operation capability is mismatched to the training data access pattern.
Question 213
After applying 'benchmark and tune metadata handling for the actual file-size distribution' in an NVIDIA AI factory, what would be the MOST relevant operational verification?
measured file-open rate and training dataloader throughput meet agreed targets
a non-root training job writes and restores a test checkpoint correctly
negative access tests block cross-tenant data while permitted training reads succeed
initiator discovery and sustained storage I/O work over the authorized path
Explanation
Correct Answer
A:
Correct. This is observable proof for the intended change, not just evidence that a command was issued. It establishes measured file-open rate and training dataloader throughput meet agreed targets, addressing metadata-operation capability is mismatched to the training data access pattern.
Incorrect Answers
B:
“a non-root training job writes and restores a test checkpoint correctly” addresses export and client mount permissions contradict the checkpoint workload, but the current hardware or infrastructure issue is metadata-operation capability is mismatched to the training data access pattern. The option “measured file-open rate and training dataloader throughput meet agreed targets” is more directly tied to the required outcome.
C:
“negative access tests block cross-tenant data while permitted training reads succeed” addresses storage identity and access isolation are absent, but the current hardware or infrastructure issue is metadata-operation capability is mismatched to the training data access pattern. The option “measured file-open rate and training dataloader throughput meet agreed targets” is more directly tied to the required outcome.
D:
“initiator discovery and sustained storage I/O work over the authorized path” addresses fabric endpoint addressing does not match the approved storage path, but the current hardware or infrastructure issue is metadata-operation capability is mismatched to the training data access pattern. The option “measured file-open rate and training dataloader throughput meet agreed targets” is more directly tied to the required outcome.
Question 214
At a GPU cluster site, an operator turns on client-side caching for mutable training labels and workers observe outdated samples. Which remediation best targets the underlying cause?
standardize mount paths and validated namespace settings across nodes
align initiator and target transport addresses with their intended network
choose and validate supported consistency and cache invalidation settings
apply tenant-scoped credentials and server-side file access rules
Explanation
Correct Answer
C:
Correct. The incident follows from storage cache coherency is unsuitable for the application's update model. This action changes the failing dependency, with updates become visible to all readers within the required consistency bound as a measurable success criterion.
Incorrect Answers
A:
“standardize mount paths and validated namespace settings across nodes” addresses filesystem mount namespace and path consistency were not established, but the current hardware or infrastructure issue is storage cache coherency is unsuitable for the application's update model. The option “choose and validate supported consistency and cache invalidation settings” is more directly tied to the required outcome.
B:
Useful for fabric endpoint addressing does not match the approved storage path, but it does not remove the specific condition: storage cache coherency is unsuitable for the application's update model.
D:
“apply tenant-scoped credentials and server-side file access rules” addresses storage identity and access isolation are absent, but the current hardware or infrastructure issue is storage cache coherency is unsuitable for the application's update model. The option “choose and validate supported consistency and cache invalidation settings” is more directly tied to the required outcome.
Question 215
For a controlled change involving 'choose and validate supported consistency and cache invalidation settings', which acceptance evidence would best establish the intended outcome?
updates become visible to all readers within the required consistency bound
initiator discovery and sustained storage I/O work over the authorized path
all workers resolve the same checkpoint URI and verify matching content hashes
end-to-end checkpoint bandwidth meets the acceptance baseline without OOB congestion
Explanation
Correct Answer
A:
Correct. The test directly checks whether storage cache coherency is unsuitable for the application's update model has been remediated. That result supports operational acceptance rather than only configuration presence.
Incorrect Answers
B:
“initiator discovery and sustained storage I/O work over the authorized path” addresses fabric endpoint addressing does not match the approved storage path, but the current hardware or infrastructure issue is storage cache coherency is unsuitable for the application's update model. The option “updates become visible to all readers within the required consistency bound” is more directly tied to the required outcome.
C:
“all workers resolve the same checkpoint URI and verify matching content hashes” addresses filesystem mount namespace and path consistency were not established, but the current hardware or infrastructure issue is storage cache coherency is unsuitable for the application's update model. The option “updates become visible to all readers within the required consistency bound” is more directly tied to the required outcome.
D:
“end-to-end checkpoint bandwidth meets the acceptance baseline without OOB congestion” addresses storage mount routing is inconsistent with performance topology, but the current hardware or infrastructure issue is storage cache coherency is unsuitable for the application's update model. The option “updates become visible to all readers within the required consistency bound” is more directly tied to the required outcome.
Question 216
During a readiness assessment, a storage vendor enables snapshots but restore testing is omitted before production deployment. Which correction should be prioritized?
configure mounts and routing over dedicated storage network interfaces
align approved NFS export permissions and mount options with least privilege
perform a versioned checkpoint restore into an isolated validation location
align initiator and target transport addresses with their intended network
Explanation
Correct Answer
C:
Correct. This is the corrective step for backup configuration was confused with verified data recoverability; it addresses the failure at its source. The confirming observation is recovered model and dataset hashes match the protected point-in-time copies.
Incorrect Answers
A:
“configure mounts and routing over dedicated storage network interfaces” addresses storage mount routing is inconsistent with performance topology, but the current hardware or infrastructure issue is backup configuration was confused with verified data recoverability. The option “perform a versioned checkpoint restore into an isolated validation location” is more directly tied to the required outcome.
B:
“align approved NFS export permissions and mount options with least privilege” addresses export and client mount permissions contradict the checkpoint workload, but the current hardware or infrastructure issue is backup configuration was confused with verified data recoverability. The option “perform a versioned checkpoint restore into an isolated validation location” is more directly tied to the required outcome.
D:
Useful for fabric endpoint addressing does not match the approved storage path, but it does not remove the specific condition: backup configuration was confused with verified data recoverability.
Question 217
A team implements the correction 'perform a versioned checkpoint restore into an isolated validation location' for a GPU cluster problem. Which observed result MOST convincingly proves it worked?
all workers resolve the same checkpoint URI and verify matching content hashes
recovered model and dataset hashes match the protected point-in-time copies
initiator discovery and sustained storage I/O work over the authorized path
the submitted job sees the remote filesystem and root-disk consumption stays stable
Explanation
Correct Answer
B:
Correct. It specifically tests the outcome of the approved remediation. The failure mechanism was backup configuration was confused with verified data recoverability; the listed result demonstrates the expected behavior.
Incorrect Answers
A:
“all workers resolve the same checkpoint URI and verify matching content hashes” addresses filesystem mount namespace and path consistency were not established, but the current hardware or infrastructure issue is backup configuration was confused with verified data recoverability. The option “recovered model and dataset hashes match the protected point-in-time copies” is more directly tied to the required outcome.
C:
“initiator discovery and sustained storage I/O work over the authorized path” addresses fabric endpoint addressing does not match the approved storage path, but the current hardware or infrastructure issue is backup configuration was confused with verified data recoverability. The option “recovered model and dataset hashes match the protected point-in-time copies” is more directly tied to the required outcome.
D:
“the submitted job sees the remote filesystem and root-disk consumption stays stable” addresses workload launch lacks a storage-readiness dependency, but the current hardware or infrastructure issue is backup configuration was confused with verified data recoverability. The option “recovered model and dataset hashes match the protected point-in-time copies” is more directly tied to the required outcome.
Question 218
During an AI-factory acceptance review, the following condition is observed: a BlueField-3 card appears in PCIe inventory but the embedded Arm operating system cannot be reached from management. What should the infrastructure engineer do FIRST?
verify embedded DPU OS boot and its supported management route independently
restrict DPU management accounts and reachability independently from workload users
stage and verify a supported signed BFB image for that DPU model
validate BlueField firmware DPU OS and DOCA compatibility before loading services
Explanation
Correct Answer
A:
Correct. This is the corrective step for host PCIe enumeration was treated as proof of DPU control-plane readiness; it addresses the failure at its source. The confirming observation is authenticated DPU management session returns platform health and operating version.
Incorrect Answers
B:
Useful for DPU administrative trust boundary was not configured for a production cluster, but it does not remove the specific condition: host PCIe enumeration was treated as proof of DPU control-plane readiness.
C:
“stage and verify a supported signed BFB image for that DPU model” addresses dPU operating-system image compatibility and provenance were not validated, but the current hardware or infrastructure issue is host PCIe enumeration was treated as proof of DPU control-plane readiness. The option “verify embedded DPU OS boot and its supported management route independently” is more directly tied to the required outcome.
D:
“validate BlueField firmware DPU OS and DOCA compatibility before loading services” addresses dPU firmware and DOCA version compatibility was not established, but the current hardware or infrastructure issue is host PCIe enumeration was treated as proof of DPU control-plane readiness. The option “verify embedded DPU OS boot and its supported management route independently” is more directly tied to the required outcome.
Question 219
After applying 'verify embedded DPU OS boot and its supported management route independently' in an NVIDIA AI factory, what would be the MOST relevant operational verification?
DPU management recovers and the host sees restored network functions
firmware install and first boot complete with the approved DPU OS identity
authenticated DPU management session returns platform health and operating version
Explanation
Correct Answer
D:
Correct. It specifically tests the outcome of the approved remediation. The failure mechanism was host PCIe enumeration was treated as proof of DPU control-plane readiness; the listed result demonstrates the expected behavior.
Incorrect Answers
A:
“DPU management recovers and the host sees restored network functions” addresses embedded DPU lifecycle is independent of the host reboot action, but the current hardware or infrastructure issue is host PCIe enumeration was treated as proof of DPU control-plane readiness. The option “authenticated DPU management session returns platform health and operating version” is more directly tied to the required outcome.
B:
“unauthorized host processes cannot reach DPU admin endpoints and logged authorized access succeeds” addresses dPU administrative trust boundary was not configured for a production cluster, but the current hardware or infrastructure issue is host PCIe enumeration was treated as proof of DPU control-plane readiness. The option “authenticated DPU management session returns platform health and operating version” is more directly tied to the required outcome.
C:
“firmware install and first boot complete with the approved DPU OS identity” addresses dPU operating-system image compatibility and provenance were not validated, but the current hardware or infrastructure issue is host PCIe enumeration was treated as proof of DPU control-plane readiness. The option “authenticated DPU management session returns platform health and operating version” is more directly tied to the required outcome.
Question 220
At a GPU cluster site, an engineer configures DPU operating mode using host settings copied from a deployment with the opposite trust model. Which remediation best targets the underlying cause?
use the supported out-of-band DPU recovery mechanism
stage and verify a supported signed BFB image for that DPU model
set the supported DPU mode according to the approved architecture and vendor procedure
verify embedded DPU OS boot and its supported management route independently
Explanation
Correct Answer
C:
Correct. This is the corrective step for BlueField mode and host ownership assumptions are incompatible with intended offload; it addresses the failure at its source. The confirming observation is host and embedded Arm control-plane behaviors match the chosen separation model.
Incorrect Answers
A:
“use the supported out-of-band DPU recovery mechanism” addresses embedded DPU lifecycle is independent of the host reboot action, but the current hardware or infrastructure issue is blueField mode and host ownership assumptions are incompatible with intended offload. The option “set the supported DPU mode according to the approved architecture and vendor procedure” is more directly tied to the required outcome.
B:
“stage and verify a supported signed BFB image for that DPU model” addresses dPU operating-system image compatibility and provenance were not validated, but the current hardware or infrastructure issue is blueField mode and host ownership assumptions are incompatible with intended offload. The option “set the supported DPU mode according to the approved architecture and vendor procedure” is more directly tied to the required outcome.
D:
Useful for host PCIe enumeration was treated as proof of DPU control-plane readiness, but it does not remove the specific condition: BlueField mode and host ownership assumptions are incompatible with intended offload.
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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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