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NS0-901 Test Preparation - Exam NS0-901 Materials

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q43-Q48):

NEW QUESTION # 43
Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)

A. TensorFlow Extended
B. KubeFlow
C. Google VertexAI
D. RunAI

Answer: B,D

NEW QUESTION # 44
The firm's data science team needs to run a high-priority, interactive model analysis job that requires immediate access to two GPUs. However, all GPUs in the cluster are currently allocated to long-running, lower-priority batch training jobs.
The MLOps platform, Run:AI, shows the following queue status:
JOB_ID | PROJECT | STATUS | PRIORITY | GPU_ALLOCATED
||--|-|
batch_job_1 | team_a | Running | Low | 2
batch_job_2 | team_a | Running | Low | 2
batch_job_3 | team_b | Running | Low | 4
interactive_1| team_c | Pending | High | 2 (requested)
How does the Run:AI platform address this resource contention to allow the high-priority job to run?

A. It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
B. It automatically pauses one of the low-priority jobs, saves its state, and allocates its GPUs to the high- priority job, placing the paused job back in the queue.
C. It automatically terminates all low-priority jobs to free up the entire cluster.
D. It sends an email notification to the administrator to manually reallocate the GPUs.

Answer: B

NEW QUESTION # 45
A data science team reports that their Jupyter notebook pod, which was previously working, is now failing to start. The pod's status is 'CrashLoopBackOff'. An MLOps engineer investigates and finds that the pod's PersistentVolumeClaim (PVC) is bound, but the pod logs show a "Permission denied" error when trying to write to its '/data' mount point.
The engineer checks the Trident backend configuration associated with the pod's StorageClass:
apiVersion: trident.netapp.io/v1
kind: TridentBackendConfig
metadata:
name: ontap-nas-eco
spec:
version: 1
storageDriverName: ontap-nas
managementLIF: 10.10.20.5
dataLIF: 10.10.20.10
svm: svm-prod-ds
exportPolicy: read-only-policy
What is the most likely cause of the "Permission denied" error?

A. The Trident backend is configured to use an export policy ('read-only-policy') that does not grant write permissions to the Kubernetes nodes.
B. The 'dataLIF' is configured incorrectly and is unreachable from the Kubernetes nodes.
C. The 'storageDriverName' should be 'ontap-san' for all AI workloads.
D. The Kubernetes pod has an invalid 'securityContext' that prevents it from writing to any volume.

Answer: A

NEW QUESTION # 46
The firm decides to implement a disaster recovery (DR) site for the "Advisor Assistant" application in a secondary data center. The Recovery Point Objective (RPO) is 15 minutes, and the Recovery Time Objective (RTO) is 4 hours. The design must protect both the document data lake and the vector database.
The primary site contains:
- Data Lake: NetApp StorageGRID
- Vector DB: NetApp AFF A-Series
Which combination of technologies and processes provides a complete and robust DR solution?
(Select all that apply.)

A. Use StorageGRID's built-in replication rules to replicate object data from the primary site's grid to a StorageGRID instance at the DR site.
B. Use BlueXP disaster recovery to orchestrate and automate the failover and failback workflows for the application and its data dependencies.
C. Use NetApp FlexCache at the DR site to cache data from the primary site.
D. Use NetApp SnapMirror to create an asynchronous replication relationship for the AFF A-Series volume containing the vector database, with a schedule of 10 minutes.
E. Rely on tape backups to be shipped to the DR site in the event of a disaster.
F. Use the NetApp DataOps Toolkit to manually script the failover process.

Answer: A,B,D

NEW QUESTION # 47
An AI training job is running slower than expected. The infrastructure team suspects a compute bottleneck. The job involves processing high-resolution images with a complex convolutional neural network (CNN). They review the logs from the training script.
Epoch 1/100 - 3600s - loss: 1.253 - acc: 0.54
...
CPU_Utilization_During_Epoch: 99% (all cores)
GPU_0_Utilization_During_Epoch: 8%
GPU_1_Utilization_During_Epoch: 7%
...
Epoch 2/100 - 3610s - loss: 1.102 - acc: 0.61
What is the most likely cause of the poor training performance?

A. The network is saturated, preventing the GPUs from receiving data.
B. The model is too simple and does not effectively utilize the GPU's parallel processing capabilities.
C. The storage system cannot deliver data fast enough to the compute node.
D. The training script is CPU-bound, likely performing data augmentation or preprocessing on the CPU instead of offloading it to the GPUs.

Answer: D

NEW QUESTION # 48
......

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