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Exam Dumps For NS0-901 - Refund Promise In The Event Of Failure

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q90-Q95):

NEW QUESTION # 90
A national research laboratory is investing in a turnkey AI infrastructure solution. Their primary goal is to eliminate the complexity and risk of designing and integrating the compute, network, and storage components themselves. The solution must be pre-validated by the vendors to deliver predictable, linear performance as they scale from one to multiple compute nodes. Which two options represent this type of pre-validated, converged infrastructure solution for AI? (Choose two)

A. NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking.
B. A Cisco and NetApp FlexPod for AI solution.
C. A custom-built server rack with components sourced from various different vendors.
D. A public cloud provider's general-purpose virtual machine instances.
E. A single NetApp FAS system connected directly to a single server.

Answer: A,B

NEW QUESTION # 91
A training job on one of the NVIDIA DGX servers is running slowly. A performance engineer runs the 'dstat' command on the server and captures the following output during the job execution.
-total-cpu-usage- -dsk/total- -net/total- paging-- system-- usr sys idl wai hiq siq| read writ| recv send| in out | int csw 15 5 70 10 0 0| 1.2G 15.0M| 1.2G 12.0M| 0 0 | 15k 35k
14 6 69 11 0 0| 1.2G 14.3M| 1.2G 11.8M| 0 0 | 14k 33k
16 5 68 11 0 0| 1.2G 16.1M| 1.2G 13.1M| 0 0 | 16k 36k
The server is connected to the NetApp ASA via a 100GbE (12.5 GB/s) network.
What is the most likely performance bottleneck based on this data?

A. The server's network interface is saturated.
B. The application is experiencing excessive memory paging.
C. The storage system (ASA) is unable to provide sufficient read throughput.
D. The server's CPU is overloaded.

Answer: A

NEW QUESTION # 92
An MLOps engineer is deploying a training pod that requires a high-performance volume. After applying the pod and PVC manifests, the pod remains in a 'Pending' state. The engineer runs
'kubectl describe pod training-pod-7d8c' and sees the following event:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 2m15s
default-scheduler 0/4 nodes are available: 1 node(s) had volume node affinity conflict, 3 node(s) didn't find available persistent volume to bind.
The engineer then inspects the associated PVC and sees its status is also 'Pending'.
What is the most likely cause of this issue?

A. The Kubernetes scheduler is malfunctioning and cannot assign pods to nodes.
B. The 'storageClassName' specified in the PVC does not match any existing StorageClass managed by Trident.
C. The Trident controller pod has crashed and needs to be restarted.
D. The ONTAP backend is out of available capacity to provision a new volume.

Answer: B

NEW QUESTION # 93
The pod running the vector database on the Kubernetes cluster fails to start. An MLOps engineer runs 'kubectl describe pod vector-db-pod-0' and sees the following event message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 30s default-scheduler 0/8 nodes are available: 8 node(s) did not match pod anti-affinity rules.
The pod's manifest contains the following 'affinity' definition:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app
operator: In
values:
- vector-db
topologyKey: "kubernetes.io/hostname"
What is the most likely reason the pod cannot be scheduled?

A. The 'topologyKey' is invalid; it should be 'failure-domain.beta.kubernetes.io/zone'.
B. The PersistentVolumeClaim for the pod is in a 'Pending' state.
C. The pod's anti-affinity rule prevents it from being scheduled on any node that is already running another pod with the label 'app=vector-db'.
D. The 'vector-db-pod-0' is requesting more GPU resources than are available on any node.

Answer: C

NEW QUESTION # 94
What is the primary reason for implementing a hybrid cloud architecture in this medical imaging scenario?

A. To leverage the powerful, elastic GPU compute resources available in the public cloud for model training while keeping sensitive source data on-premises to meet security and HIPAA compliance requirements.
B. To simplify network management by using a single, global virtual network spanning the on- premises and cloud environments.
C. To reduce the overall cost of storage by keeping all data in the public cloud.
D. To exclusively use cloud-native AI services, eliminating the need for any on-premises hardware.

Answer: A

NEW QUESTION # 95
......

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