NS0-901 Exams Collection, Valid NS0-901 Dumps

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Cloud and Hybrid Cloud AI Deployment	18%	- Data mobility and consistency across environments - NetApp cloud data services for AI - Cloud-native AI solutions and integration - Hybrid and multi-cloud AI architectures
Topic 2: Security, Reliability, and Operations	15%	- High availability and data protection - Cost management and efficiency - Data security and access control for AI - Monitoring, logging, and troubleshooting AI environments
Topic 3: NetApp AI Solutions and Architecture	25%	- NetApp AI-ready infrastructure components - Storage architectures for AI workloads - ONTAP integration with AI frameworks - Scalability and performance optimization for AI - Data management and data pipeline design
Topic 4: AI Lifecycle	27%	- Data preparation and management for AI - AI governance, ethics, and compliance - AI lifecycle stages: design, training, deployment, monitoring - Predictive vs generative AI - Model training, inference, and optimization
Topic 5: AI Overview	15%	- AI deployment models: on-premises, cloud, edge - AI, machine learning, and deep learning concepts - Convergence of AI, high-performance computing, and analytics - AI industry use cases and applications - Algorithm types: supervised, unsupervised, reinforcement learning

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q86-Q91):

NEW QUESTION # 86
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 training script is CPU-bound, likely performing data augmentation or preprocessing on the CPU instead of offloading it to the GPUs.
D. The storage system cannot deliver data fast enough to the compute node.

Answer: C

NEW QUESTION # 87
Advisors report that some queries to the chatbot are unacceptably slow, taking several seconds to respond. The MLOps team isolates the issue to the RAG retrieval step. Performance monitoring of the NetApp AFF A-Series hosting the vector database shows the following metrics during periods of high query load.
avg_read_latency: 3500 microseconds (3.5 ms)
avg_write_latency: 400 microseconds (0.4 ms)
iops_total: 15,000
cpu_utilization_storage_node: 15%
workload_profile: 95% small, random reads
Given these metrics, what is the most likely performance bottleneck?

A. The storage node CPU is the bottleneck, preventing faster data processing.
B. The AFF A-Series system is underpowered and cannot handle the IOPS load.
C. The vector database is poorly indexed, causing inefficient, slow lookups that translate to high read latency on the storage.
D. The network between the application and the storage system is saturated.

Answer: C

NEW QUESTION # 88
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 NetApp FlexCache at the DR site to cache data from the primary site.
B. 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.
C. 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.
D. Use BlueXP disaster recovery to orchestrate and automate the failover and failback workflows for the application and its data dependencies.
E. Use the NetApp DataOps Toolkit to manually script the failover process.
F. Rely on tape backups to be shipped to the DR site in the event of a disaster.

Answer: B,C,D

NEW QUESTION # 89
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 # 90
Which of the following are typical requirements for inferencing in AI models? (Choose two)

A. Pre-trained models
B. Cloud-based inference only
C. Loading the model into memory
D. Extensive training data

Answer: A,C

NEW QUESTION # 91
......

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